{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import glob\n",
    "from skimage.io import imread\n",
    "from skimage.transform import resize\n",
    "from tensorflow.python.framework.ops import reset_default_graph\n",
    "\n",
    "def onehot(t, num_classes):\n",
    "    out = np.zeros((t.shape[0], num_classes))\n",
    "    for row, col in enumerate(t):\n",
    "        out[row, col] = 1\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle challenge\n",
    "\n",
    "In this lab we will work on a data science challenge from `kaggle.com`.\n",
    "Kaggle is a website to participate in real life challenges.\n",
    "Most competitions on kaggle have a dataset, an accuracy metric and a leaderboard to compare submissions.\n",
    "You can read more about kaggle [here](https://www.kaggle.com/about).\n",
    "\n",
    "OBS: You will need a kaggle account for this exercise!\n",
    "\n",
    "The challenge we will pursue is the [_Leaf Classification_](https://www.kaggle.com/c/leaf-classification) challenge.\n",
    "The dataset consists approximately 1,584 images of leaf specimens (16 samples each of 99 species) which have been converted to binary black leaves against white backgrounds. Three sets of features are also provided per image: a shape contiguous descriptor, an interior texture histogram, and a ﬁne-scale margin histogram. For each feature, a 64-attribute vector is given per leaf sample.\n",
    "\n",
    "The first task in a kaggle competition is to download, understand and preprocess the data. This we will do in the first section.\n",
    "\n",
    "Afterwards, we will look into the type of neural network best suited for handling this type of data. For images, usually the convolutional neural network does a pretty good job, for timeseries (like the shape) usually the RNN is the network of choice.\n",
    "\n",
    "Lastly, we will train the model and put the outputs in a submission file that we can submit to kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data\n",
    "\n",
    "Go to the [data section](https://www.kaggle.com/c/leaf-classification/data) of the Leaf Classification competition on kaggle.\n",
    "\n",
    "Next, download all of the available data (`sample_submission.csv`, `train.csv`, `test.csv`, `images`), accept the disclaimer if asked and unzip all folders into the `lab6` folder.\n",
    "Such that\n",
    "\n",
    "```\n",
    ">ls $PATH\\_TO\\_FOLDER/tensorflow_tutorial/lab6\n",
    "images  lab6_Kaggle.ipynb  README.md  sample_submission.csv  test.csv  train.csv\n",
    "```\n",
    "\n",
    "Below we will try to load the data into memory and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of images = 1584\n"
     ]
    }
   ],
   "source": [
    "image_paths = glob.glob(\"images/*\")\n",
    "print \"Amount of images =\", len(image_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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LzFYCmNkKYPcYvnny3MhStkys2/AsWbKEKVOmIImxY8cyefJkJk+ezNy5c5k8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\n4xSBG47jFIEbjuMUgRuO4xTB/wBXyp6DFoSYmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f87242b2610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfe66110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfce0890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p3bs3M2bMoGHDhj4uzlMt9tprL1asWJG1/Aq60+SWW25h5syZzJ492xubp9rM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\ncB5PjHiD83hixBucxxMj3uA8nhj5/8gTAb7J7Hv9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfc71790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JHH/88QkrzB/77LNP2hJKk1wtWVILRWDFAdtpp51y8pbMshtUme3yzDPP5FR3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\nFB1umBzHKTrcMDmOU3T8f8Sra6848IkLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfcefb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TY4yobnk3fG5DXHjhhXz66aeezJ1zzZJPyRwKtMklasCAAcycObMlTuWcK1Cn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfadf890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D8d1kxN9+DB6zPEGUDSTe2AmUJ4iygVx33XWdgjz99NNTtia7hDploCJCnTLQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfcf96d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MnZvZtDU7qJYfbkstd6tS7oEDB/LGG280WqZv376sXOk2Xs2aS5JPnz59WLt2bWgxmiQq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\nuPTSS0OLsAuHHnp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfa8b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D1157jYEDBzZnP4XZl6o+6RidzLliIwlJvPPOO5nZf6GWcMAbjZ1rqubWDIqu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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfb8ab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UFTOrScUoF7ccKdKIFqRWm09uOarM1q1bueiii7IWw0kJtxwVpB6jnBSel40b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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfe22c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now plot 10 images\n",
    "# as we need all images to have the same dimensionality, we will resize and plot\n",
    "# make the images as small as possible, until the difference between starts to get blurry\n",
    "for i in range(10):\n",
    "    image = imread(image_paths[i], as_grey=True)\n",
    "    #image = resize(image, output_shape=(100, 100))\n",
    "    plt.imshow(image, cmap='gray')\n",
    "    plt.title(\"name: %s \\n shape:%s\" % (image_paths[i], image.shape))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>species</th>\n",
       "      <th>margin1</th>\n",
       "      <th>margin2</th>\n",
       "      <th>margin3</th>\n",
       "      <th>margin4</th>\n",
       "      <th>margin5</th>\n",
       "      <th>margin6</th>\n",
       "      <th>margin7</th>\n",
       "      <th>margin8</th>\n",
       "      <th>...</th>\n",
       "      <th>texture55</th>\n",
       "      <th>texture56</th>\n",
       "      <th>texture57</th>\n",
       "      <th>texture58</th>\n",
       "      <th>texture59</th>\n",
       "      <th>texture60</th>\n",
       "      <th>texture61</th>\n",
       "      <th>texture62</th>\n",
       "      <th>texture63</th>\n",
       "      <th>texture64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>1575</td>\n",
       "      <td>Magnolia_Salicifolia</td>\n",
       "      <td>0.060547</td>\n",
       "      <td>0.119140</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.148440</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.242190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010742</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>1578</td>\n",
       "      <td>Acer_Pictum</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.107420</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018555</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>1581</td>\n",
       "      <td>Alnus_Maximowiczii</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.016602</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>1582</td>\n",
       "      <td>Quercus_Rubra</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.056641</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083008</td>\n",
       "      <td>0.030273</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.014648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.002930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>1584</td>\n",
       "      <td>Quercus_Afares</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.022461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 194 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id               species   margin1   margin2   margin3   margin4  \\\n",
       "985  1575  Magnolia_Salicifolia  0.060547  0.119140  0.007812  0.003906   \n",
       "986  1578           Acer_Pictum  0.001953  0.003906  0.021484  0.107420   \n",
       "987  1581    Alnus_Maximowiczii  0.001953  0.003906  0.000000  0.021484   \n",
       "988  1582         Quercus_Rubra  0.000000  0.000000  0.046875  0.056641   \n",
       "989  1584        Quercus_Afares  0.023438  0.019531  0.031250  0.015625   \n",
       "\n",
       "      margin5   margin6   margin7  margin8    ...      texture55  texture56  \\\n",
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       "\n",
       "     texture57  texture58  texture59  texture60  texture61  texture62  \\\n",
       "985   0.034180   0.000000   0.010742   0.000000   0.000000   0.000000   \n",
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       "989   0.002930   0.000000   0.012695   0.000000   0.000000   0.023438   \n",
       "\n",
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       "989   0.025391   0.022461  \n",
       "\n",
       "[5 rows x 194 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now loading the train.csv to find features for each training point\n",
    "train = pd.read_csv('train.csv')\n",
    "# notice how we \"only\" have 990 (989+0 elem) images for training, the rest is for testing\n",
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "</table>\n",
       "<p>5 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id   margin1   margin2   margin3   margin4   margin5   margin6  \\\n",
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       "\n",
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       "592   0.000000   0.011719        0.0        0.0   0.000000   0.011719   \n",
       "593   0.000977   0.004883        0.0        0.0   0.015625   0.000000   \n",
       "\n",
       "     texture64  \n",
       "589   0.000977  \n",
       "590   0.016602  \n",
       "591   0.006836  \n",
       "592   0.018555  \n",
       "593   0.017578  \n",
       "\n",
       "[5 rows x 193 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now do similar as in train example above for test.csv\n",
    "test = pd.read_csv('test.csv')\n",
    "# notice that we do not have species here, we need to predict that ..!\n",
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Acer_Capillipes</th>\n",
       "      <th>Acer_Circinatum</th>\n",
       "      <th>Acer_Mono</th>\n",
       "      <th>Acer_Opalus</th>\n",
       "      <th>Acer_Palmatum</th>\n",
       "      <th>Acer_Pictum</th>\n",
       "      <th>Acer_Platanoids</th>\n",
       "      <th>Acer_Rubrum</th>\n",
       "      <th>Acer_Rufinerve</th>\n",
       "      <th>...</th>\n",
       "      <th>Salix_Fragilis</th>\n",
       "      <th>Salix_Intergra</th>\n",
       "      <th>Sorbus_Aria</th>\n",
       "      <th>Tilia_Oliveri</th>\n",
       "      <th>Tilia_Platyphyllos</th>\n",
       "      <th>Tilia_Tomentosa</th>\n",
       "      <th>Ulmus_Bergmanniana</th>\n",
       "      <th>Viburnum_Tinus</th>\n",
       "      <th>Viburnum_x_Rhytidophylloides</th>\n",
       "      <th>Zelkova_Serrata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
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       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
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       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
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       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>1580</td>\n",
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       "      <td>0.010101</td>\n",
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       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>1583</td>\n",
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       "      <td>0.010101</td>\n",
       "      <td>0.010101</td>\n",
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       "      <td>0.010101</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  Acer_Capillipes  Acer_Circinatum  Acer_Mono  Acer_Opalus  \\\n",
       "589  1576         0.010101         0.010101   0.010101     0.010101   \n",
       "590  1577         0.010101         0.010101   0.010101     0.010101   \n",
       "591  1579         0.010101         0.010101   0.010101     0.010101   \n",
       "592  1580         0.010101         0.010101   0.010101     0.010101   \n",
       "593  1583         0.010101         0.010101   0.010101     0.010101   \n",
       "\n",
       "     Acer_Palmatum  Acer_Pictum  Acer_Platanoids  Acer_Rubrum  Acer_Rufinerve  \\\n",
       "589       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
       "590       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
       "591       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
       "592       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
       "593       0.010101     0.010101         0.010101     0.010101        0.010101   \n",
       "\n",
       "          ...         Salix_Fragilis  Salix_Intergra  Sorbus_Aria  \\\n",
       "589       ...               0.010101        0.010101     0.010101   \n",
       "590       ...               0.010101        0.010101     0.010101   \n",
       "591       ...               0.010101        0.010101     0.010101   \n",
       "592       ...               0.010101        0.010101     0.010101   \n",
       "593       ...               0.010101        0.010101     0.010101   \n",
       "\n",
       "     Tilia_Oliveri  Tilia_Platyphyllos  Tilia_Tomentosa  Ulmus_Bergmanniana  \\\n",
       "589       0.010101            0.010101         0.010101            0.010101   \n",
       "590       0.010101            0.010101         0.010101            0.010101   \n",
       "591       0.010101            0.010101         0.010101            0.010101   \n",
       "592       0.010101            0.010101         0.010101            0.010101   \n",
       "593       0.010101            0.010101         0.010101            0.010101   \n",
       "\n",
       "     Viburnum_Tinus  Viburnum_x_Rhytidophylloides  Zelkova_Serrata  \n",
       "589        0.010101                      0.010101         0.010101  \n",
       "590        0.010101                      0.010101         0.010101  \n",
       "591        0.010101                      0.010101         0.010101  \n",
       "592        0.010101                      0.010101         0.010101  \n",
       "593        0.010101                      0.010101         0.010101  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now do similar as in train example above for test.csv\n",
    "sample_submission = pd.read_csv('sample_submission.csv')\n",
    "# accordingly to these IDs we need to provide the probability of a given plant being present\n",
    "sample_submission.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'margin1', u'shape1', u'texture1'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# name all columns in train, should be 3 different columns with 64 values each\n",
    "print train.columns[2::64]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape, (990, 192)\n",
      "margin.shape, (990, 64)\n",
      "shape.shape, (990, 64)\n",
      "texture.shape, (990, 64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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D6TRmdrqZTTWz6Wb22Zjre5nZeDObYWaPmdmIgutHmNlqM7uq1HayXLTN2boV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f86bfcc7f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try and extract and plot columns\n",
    "X = train.as_matrix(columns=train.columns[2:])\n",
    "print \"X.shape,\", X.shape\n",
    "margin = X[:, :64]\n",
    "shape = X[:, 64:128]\n",
    "texture = X[:, 128:]\n",
    "print \"margin.shape,\", margin.shape\n",
    "print \"shape.shape,\", shape.shape\n",
    "print \"texture.shape,\", texture.shape\n",
    "# let us plot some of the features\n",
    "plt.figure(figsize=(21,7))\n",
    "for i in range(3):\n",
    "    plt.subplot(3,3,1+i*3)\n",
    "    plt.plot(margin[i])\n",
    "    if i == 0:\n",
    "        plt.title('Margin', fontsize=20)\n",
    "    plt.axis('off')\n",
    "    plt.subplot(3,3,2+i*3)\n",
    "    plt.plot(shape[i])\n",
    "    if i == 0:\n",
    "        plt.title('Shape', fontsize=20)\n",
    "    plt.axis('off')\n",
    "    plt.subplot(3,3,3+i*3)\n",
    "    plt.plot(texture[i])\n",
    "    if i == 0:\n",
    "        plt.title('Texture', fontsize=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "1. Test various resizings of the image until you have found the smallest resizing of the image where you can still see differentiate between the images. Use IMAGE_SHAPE=(?, ?, 1) to reflect your choices.\n",
    "\n",
    "So far we have learned about the feed forward neural network, the convolutional neural network and the recurrent neural network.\n",
    "Given margin and texture are histograms, shape is a contigious value over a \"time\" dimension \n",
    "\n",
    "2. How would could Margin, Shape and Texture be represented for classification?\n",
    "\n",
    "3. Describe what network you would build and how you would represent the data points (image, margin, shape and texture)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Building data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import glob\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from skimage.io import imread\n",
    "from skimage.transform import resize\n",
    "from tensorflow.python.framework.ops import reset_default_graph\n",
    "import os\n",
    "import subprocess\n",
    "import itertools\n",
    "from datetime import datetime\n",
    "\n",
    "def onehot(t, num_classes):\n",
    "    out = np.zeros((t.shape[0], num_classes))\n",
    "    for row, col in enumerate(t):\n",
    "        out[row, col] = 1\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class load_data():\n",
    "    # data_train, data_test and le are public\n",
    "    def __init__(self, train_path, test_path, image_paths, image_shape=(128, 128)):\n",
    "        train_df = pd.read_csv(train_path)\n",
    "        test_df = pd.read_csv(test_path)\n",
    "        image_paths = image_paths\n",
    "        image_shape = image_shape\n",
    "        self._load(train_df, test_df, image_paths, image_shape)\n",
    "        \n",
    "    def _load(self, train_df, test_df, image_paths, image_shape):\n",
    "        print \"loading data ...\"\n",
    "        # load train.csv\n",
    "        path_dict = self._path_to_dict(image_paths) # numerate image paths and make it a dict\n",
    "        # merge image paths with data frame\n",
    "        train_image_df = self._merge_image_df(train_df, path_dict)\n",
    "        test_image_df = self._merge_image_df(test_df, path_dict)\n",
    "        # label encoder-decoder (self. because we need it later)\n",
    "        self.le = LabelEncoder().fit(train_image_df['species'])\n",
    "        # labels for train\n",
    "        t_train = self.le.transform(train_image_df['species'])\n",
    "        # getting data\n",
    "        train_data = self._make_dataset(train_image_df, image_shape, t_train)\n",
    "        test_data = self._make_dataset(test_image_df, image_shape)        \n",
    "        # need to reformat the train for validation split reasons in the batch_generator\n",
    "        self.train = self._format_dataset(train_data, for_train=True)\n",
    "        self.test = self._format_dataset(test_data, for_train=False)\n",
    "        print \"data loaded\"\n",
    "        \n",
    "\n",
    "    def _path_to_dict(self, image_paths):\n",
    "        path_dict = dict()\n",
    "        for image_path in image_paths:\n",
    "            num_path = int(os.path.basename(image_path[:-4]))\n",
    "            path_dict[num_path] = image_path\n",
    "        return path_dict\n",
    "\n",
    "    def _merge_image_df(self, df, path_dict):\n",
    "        split_path_dict = dict()\n",
    "        for index, row in df.iterrows():\n",
    "            split_path_dict[row['id']] = path_dict[row['id']]\n",
    "        image_frame = pd.DataFrame(split_path_dict.values(), columns=['image'])\n",
    "        df_image =  pd.concat([image_frame, df], axis=1)\n",
    "        return df_image\n",
    "    \n",
    "\n",
    "    def _make_dataset(self, df, image_shape, t_train=None):\n",
    "        if t_train is not None:\n",
    "            print \"loading train ...\"\n",
    "        else:\n",
    "            print \"loading test ...\"\n",
    "        # make dataset\n",
    "        data = dict()\n",
    "        # merge image with 3x64 features\n",
    "        for i, dat in enumerate(df.iterrows()):\n",
    "            index, row = dat\n",
    "            sample = dict()\n",
    "            if t_train is not None:\n",
    "                features = row.drop(['id', 'species', 'image'], axis=0).values\n",
    "            else:\n",
    "                features = row.drop(['id', 'image'], axis=0).values\n",
    "            sample['margin'] = features[:64]\n",
    "            sample['shape'] = features[64:128]\n",
    "            sample['texture'] = features[128:]\n",
    "            if t_train is not None:\n",
    "                sample['t'] = np.asarray(t_train[i], dtype='int32')\n",
    "            image = imread(row['image'], as_grey=True)\n",
    "            image = resize(image, output_shape=image_shape)\n",
    "            image = np.expand_dims(image, axis=2)\n",
    "            sample['image'] = image   \n",
    "            data[row['id']] = sample\n",
    "            if i % 100 == 0:\n",
    "                print \"\\t%d of %d\" % (i, len(df))\n",
    "        return data\n",
    "\n",
    "    def _format_dataset(self, df, for_train):\n",
    "        # making arrays with all data in, is nessesary when doing validation split\n",
    "        data = dict()\n",
    "        value = df.values()[0]\n",
    "        img_tot_shp = tuple([len(df)] + list(value['image'].shape))\n",
    "        data['images'] = np.zeros(img_tot_shp, dtype='float32')\n",
    "        feature_tot_shp = (len(df), 64)\n",
    "        data['margins'] = np.zeros(feature_tot_shp, dtype='float32')\n",
    "        data['shapes'] = np.zeros(feature_tot_shp, dtype='float32')\n",
    "        data['textures'] = np.zeros(feature_tot_shp, dtype='float32')\n",
    "        if for_train:\n",
    "            data['ts'] = np.zeros((len(df),), dtype='int32')\n",
    "        else:\n",
    "            data['ids'] = np.zeros((len(df),), dtype='int32')\n",
    "        for i, pair in enumerate(df.items()):\n",
    "            key, value = pair\n",
    "            data['images'][i] = value['image']\n",
    "            data['margins'][i] = value['margin']\n",
    "            data['shapes'][i] = value['shape']\n",
    "            data['textures'][i] = value['texture']\n",
    "            if for_train:\n",
    "                data['ts'][i] = value['t']\n",
    "            else:\n",
    "                data['ids'][i] = key\n",
    "        return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data ...\n",
      "loading train ...\n",
      "\t0 of 990\n",
      "\t100 of 990\n",
      "\t200 of 990\n",
      "\t300 of 990\n",
      "\t400 of 990\n",
      "\t500 of 990\n",
      "\t600 of 990\n",
      "\t700 of 990\n",
      "\t800 of 990\n",
      "\t900 of 990\n",
      "loading test ...\n",
      "\t0 of 594\n",
      "\t100 of 594\n",
      "\t200 of 594\n",
      "\t300 of 594\n",
      "\t400 of 594\n",
      "\t500 of 594\n",
      "data loaded\n",
      "\n",
      "@@@Shape checking of data sets@@@\n",
      "\n",
      "TRAIN\n",
      "\timages\t(990, 128, 128, 1)0.462177\n",
      "\tmargins\t(990, 64)\t0.015625\n",
      "\tshapes\t(990, 64)\t0.000607\n",
      "\ttextures(990, 64)\t0.015625\n",
      "\tts\t 990\n",
      "\twhile training, batch_generator will onehot encode ts to (batch_size, num_classes)\n",
      "\n",
      "TEST\n",
      "\timages\t(594, 128, 128, 1)\t0.463148\n",
      "\tmargins\t(594, 64)\t0.015625\n",
      "\tshapes\t(594, 64)\t0.000604\n",
      "\ttextures(594, 64)\t0.015625\n",
      "\tids\t594\n"
     ]
    }
   ],
   "source": [
    "# loading data and setting up constants\n",
    "TRAIN_PATH = \"train.csv\"\n",
    "TEST_PATH = \"test.csv\"\n",
    "IMAGE_PATHS = glob.glob(\"images/*.jpg\")\n",
    "NUM_CLASSES = 99\n",
    "IMAGE_SHAPE = (128, 128, 1)\n",
    "NUM_FEATURES = 64 # for all three features, margin, shape and texture\n",
    "# train holds both X (input) and t (target/truth)\n",
    "data = load_data(train_path=TRAIN_PATH, test_path=TEST_PATH,\n",
    "                 image_paths=IMAGE_PATHS, image_shape=IMAGE_SHAPE[:2])\n",
    "# to visualize the size of the dimensions of the data\n",
    "print\n",
    "print \"@@@Shape checking of data sets@@@\"\n",
    "print\n",
    "print \"TRAIN\"\n",
    "print \"\\timages\\t%s%f\" % (data.train['images'].shape, data.train['images'].mean())\n",
    "print \"\\tmargins\\t%s\\t%f\" % (data.train['margins'].shape, data.train['margins'].mean())\n",
    "print \"\\tshapes\\t%s\\t%f\" % (data.train['shapes'].shape, data.train['shapes'].mean())\n",
    "print \"\\ttextures%s\\t%f\" % (data.train['textures'].shape, data.train['textures'].mean())\n",
    "print \"\\tts\\t %s\" % (data.train['ts'].shape)\n",
    "print \"\\twhile training, batch_generator will onehot encode ts to (batch_size, num_classes)\"\n",
    "print\n",
    "print \"TEST\"\n",
    "print \"\\timages\\t%s\\t%f\" % (data.test['images'].shape, data.test['images'].mean()) \n",
    "print \"\\tmargins\\t%s\\t%f\" % (data.test['margins'].shape, data.test['margins'].mean())\n",
    "print \"\\tshapes\\t%s\\t%f\" % (data.test['shapes'].shape, data.test['shapes'].mean())\n",
    "print \"\\ttextures%s\\t%f\" % (data.test['textures'].shape, data.test['textures'].mean())\n",
    "print \"\\tids\\t%s\" % (data.test['ids'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# batch generator\n",
    "\n",
    "While training, we will not directly access the entire dataset, instead we have a `batch_generator` function to give us inputs aligned with their targets/ids in a size that our model can handle in memory (batch\\_size).\n",
    "\n",
    "Furthermore, the `batch_generator` also handles validation splitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class batch_generator():\n",
    "    def __init__(self, data, batch_size=64, num_classes=99,\n",
    "                 num_iterations=5e3, num_features=64, seed=42, val_size=0.1):\n",
    "        print \"initiating batch generator\"\n",
    "        self._train = data.train\n",
    "        self._test = data.test\n",
    "        # get image size\n",
    "        value = self._train['images'][0]\n",
    "        self._image_shape = list(value.shape)\n",
    "        self._batch_size = batch_size\n",
    "        self._num_classes = num_classes\n",
    "        self._num_iterations = num_iterations\n",
    "        self._num_features = num_features\n",
    "        self._seed = seed\n",
    "        self._val_size = 0.1\n",
    "        self._valid_split()\n",
    "        print \"batch generator initiated ...\"\n",
    "\n",
    "    def _valid_split(self):\n",
    "        self._idcs_train, self._idcs_valid = iter(\n",
    "            StratifiedShuffleSplit(self._train['ts'],\n",
    "                                   n_iter=1,\n",
    "                                   test_size=self._val_size,\n",
    "                                   random_state=self._seed)).next()\n",
    "    def _shuffle_train(self):\n",
    "        np.random.shuffle(self._idcs_train)\n",
    "\n",
    "    def _batch_init(self, purpose):\n",
    "        assert purpose in ['train', 'valid', 'test']\n",
    "        batch_holder = dict()\n",
    "        batch_holder['margins'] = np.zeros((self._batch_size, self._num_features), dtype='float32')\n",
    "        batch_holder['shapes'] = np.zeros((self._batch_size, self._num_features), dtype='float32')\n",
    "        batch_holder['textures'] = np.zeros((self._batch_size, self._num_features), dtype='float32')\n",
    "        batch_holder['images'] = np.zeros(tuple([self._batch_size] + self._image_shape), dtype='float32')\n",
    "        if (purpose == \"train\") or (purpose == \"valid\"):\n",
    "            batch_holder['ts'] = np.zeros((self._batch_size, self._num_classes), dtype='float32')          \n",
    "        else:\n",
    "            batch_holder['ids'] = []\n",
    "        return batch_holder\n",
    "\n",
    "    def gen_valid(self):\n",
    "        batch = self._batch_init(purpose='train')\n",
    "        i = 0\n",
    "        for idx in self._idcs_valid:\n",
    "            batch['margins'][i] = self._train['margins'][idx]\n",
    "            batch['shapes'][i] = self._train['shapes'][idx]\n",
    "            batch['textures'][i] = self._train['textures'][idx]\n",
    "            batch['images'][i] = self._train['images'][idx]\n",
    "            batch['ts'][i] = onehot(np.asarray([self._train['ts'][idx]], dtype='float32'), self._num_classes)\n",
    "            i += 1\n",
    "            if i >= self._batch_size:\n",
    "                yield batch, i\n",
    "                batch = self._batch_init(purpose='valid')\n",
    "                i = 0\n",
    "        if i != 0:\n",
    "            yield batch, i\n",
    "\n",
    "    def gen_test(self):\n",
    "        batch = self._batch_init(purpose='test')\n",
    "        i = 0\n",
    "        for idx in range(len(self._test['ids'])):\n",
    "            batch['margins'][i] = self._test['margins'][idx]\n",
    "            batch['shapes'][i] = self._test['shapes'][idx]\n",
    "            batch['textures'][i] = self._test['textures'][idx]\n",
    "            batch['images'][i] = self._test['images'][idx]\n",
    "            batch['ids'].append(self._test['ids'][idx])\n",
    "            i += 1\n",
    "            if i >= self._batch_size:\n",
    "                yield batch, i\n",
    "                batch = self._batch_init(purpose='test')\n",
    "                i = 0\n",
    "        if i != 0:\n",
    "            yield batch, i\n",
    "            \n",
    "\n",
    "    def gen_train(self):\n",
    "        batch = self._batch_init(purpose='train')\n",
    "        iteration = 0\n",
    "        i = 0\n",
    "        while True:\n",
    "            # shuffling all batches\n",
    "            self._shuffle_train()\n",
    "            for idx in self._idcs_train:\n",
    "                # extract data from dict\n",
    "                batch['margins'][i] = self._train['margins'][idx]\n",
    "                batch['shapes'][i] = self._train['shapes'][idx]\n",
    "                batch['textures'][i] = self._train['textures'][idx]\n",
    "                batch['images'][i] = self._train['images'][idx]\n",
    "                batch['ts'][i] = onehot(np.asarray([self._train['ts'][idx]], dtype='float32'), self._num_classes)\n",
    "                i += 1\n",
    "                if i >= self._batch_size:\n",
    "                    yield batch\n",
    "                    batch = self._batch_init(purpose='train')\n",
    "                    i = 0\n",
    "                    iteration += 1\n",
    "                    if iteration >= self._num_iterations:\n",
    "                        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initiating batch generator\n",
      "batch generator initiated ...\n",
      "\n",
      "@@@Shape/mean checking of batches@@@\n",
      "\n",
      "TRAIN\n",
      "\timages, (64, 128, 128, 1)\n",
      "\tmargins, (64, 64)\n",
      "\tshapes, (64, 64)\n",
      "\ttextures, (64, 64)\n",
      "\tts, (64, 99)\n",
      "\n",
      "VALID\n",
      "\timages, (64, 128, 128, 1)\n",
      "\tmargins, (64, 64)\n",
      "\tshapes, (64, 64)\n",
      "\ttextures, (64, 64)\n",
      "\tts, (64, 99)\n",
      "\n",
      "TEST\n",
      "\timages, (64, 128, 128, 1)\n",
      "\tmargins, (64, 64)\n",
      "\tshapes, (64, 64)\n",
      "\ttextures, (64, 64)\n",
      "\tids, 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:21: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "dummy_batch_gen = batch_generator(data, batch_size=64, num_classes=99, num_iterations=5e3, seed=42)\n",
    "train_batch = dummy_batch_gen.gen_train().next()\n",
    "valid_batch, i = dummy_batch_gen.gen_valid().next()\n",
    "test_batch, i = dummy_batch_gen.gen_test().next()\n",
    "\n",
    "print\n",
    "print \"@@@Shape/mean checking of batches@@@\"\n",
    "print\n",
    "print \"TRAIN\"\n",
    "print \"\\timages,\", train_batch['images'].shape\n",
    "print \"\\tmargins,\", train_batch['margins'].shape\n",
    "print \"\\tshapes,\", train_batch['shapes'].shape\n",
    "print \"\\ttextures,\", train_batch['textures'].shape\n",
    "print \"\\tts,\", train_batch['ts'].shape\n",
    "print\n",
    "print \"VALID\"\n",
    "print \"\\timages,\", valid_batch['images'].shape\n",
    "print \"\\tmargins,\", valid_batch['margins'].shape\n",
    "print \"\\tshapes,\", valid_batch['shapes'].shape\n",
    "print \"\\ttextures,\", valid_batch['textures'].shape\n",
    "print \"\\tts,\", valid_batch['ts'].shape\n",
    "print\n",
    "print \"TEST\"\n",
    "print \"\\timages,\", test_batch['images'].shape\n",
    "print \"\\tmargins,\", test_batch['margins'].shape\n",
    "print \"\\tshapes,\", test_batch['shapes'].shape\n",
    "print \"\\ttextures,\", test_batch['textures'].shape\n",
    "print \"\\tids,\", len(test_batch['ids'])\n",
    "# notice that mean is very different, which is why we use batch_norm in all input data in model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Documentation on contrib layers\n",
    "Check out the [github page](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/layers.py) for information on contrib layers (not well documented in their api)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# contrib layers similar to wrappings used in Lasagne (for theano) or Keras\n",
    "from tensorflow.contrib.layers import fully_connected, convolution2d, flatten, batch_norm, max_pool2d, dropout\n",
    "from tensorflow.python.ops.nn import relu, elu, relu6, sigmoid, tanh, softmax\n",
    "from tensorflow.python.ops.nn import dynamic_rnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# wrapping conv with batch_norm\n",
    "def conv(l_in, num_outputs, kernel_size, scope, stride=1):\n",
    "    return convolution2d(l_in, num_outputs=num_outputs, kernel_size=kernel_size,\n",
    "                         stride=stride, normalizer_fn=batch_norm, scope=scope)\n",
    "\n",
    "# pre-activation: http://arxiv.org/abs/1603.05027\n",
    "# wrapping convolutions and batch_norm\n",
    "def conv_pre(l_in, num_outputs, kernel_size, scope, stride=1):\n",
    "    l_norm = batch_norm(l_in)\n",
    "    l_relu = relu(l_norm)\n",
    "    return convolution2d(l_relu, num_outputs=num_outputs, kernel_size=kernel_size,\n",
    "                         stride=stride, activation_fn=None, scope=scope)\n",
    "# easy to use pool function\n",
    "def pool(l_in, scope, kernel_size=(3, 3)):\n",
    "    return max_pool2d(l_in, kernel_size=kernel_size, scope=scope) # (3, 3) has shown to work better than (2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'features_bn/beta_summary:0' shape=() dtype=string>,\n",
       " <tf.Tensor 'features_bn/moving_mean_summary:0' shape=() dtype=string>,\n",
       " <tf.Tensor 'features_bn/moving_variance_summary:0' shape=() dtype=string>,\n",
       " <tf.Tensor 'y/weights_summary:0' shape=() dtype=string>,\n",
       " <tf.Tensor 'y/biases_summary:0' shape=() dtype=string>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hyperameters of the model\n",
    "height, width, channels = IMAGE_SHAPE\n",
    "# resetting the graph ...\n",
    "reset_default_graph()\n",
    "\n",
    "# Setting up placeholder, this is where your data enters the graph!\n",
    "x_image_pl = tf.placeholder(tf.float32, [None, height, width, channels], name=\"x_image_pl\")\n",
    "x_margin_pl = tf.placeholder(tf.float32, [None, NUM_FEATURES], name=\"x_margin_pl\")\n",
    "x_shape_pl = tf.placeholder(tf.float32, [None, NUM_FEATURES], name=\"x_shape_pl\")\n",
    "x_texture_pl = tf.placeholder(tf.float32, [None, NUM_FEATURES], name=\"x_texture_pl\")\n",
    "is_training_pl = tf.placeholder(tf.bool, name=\"is_training_pl\")\n",
    "\n",
    "# Building the layers of the neural network\n",
    "# we define the variable scope, so we more easily can recognise our variables later\n",
    "\n",
    "## IMAGE\n",
    "#l_conv1_a = conv(x_image_pl, 16, (5, 5), scope=\"l_conv1_a\")\n",
    "#l_pool1 = pool(l_conv1_a, scope=\"l_pool1\")\n",
    "#l_conv2_a = conv(l_pool1, 16, (5, 5), scope=\"l_conv2_a\")\n",
    "#l_pool2 = pool(l_conv2_a, scope=\"l_pool2\")\n",
    "#l_conv3_a = conv(l_pool2, 16, (5, 5), scope=\"l_conv3_a\")\n",
    "#l_pool3 = pool(l_conv3_a, scope=\"l_pool3\")\n",
    "#l_conv4_a = conv(l_pool3, 16, (5, 5), scope=\"l_conv4_a\")\n",
    "#l_pool4 = pool(l_conv3_a, scope=\"l_pool4\")\n",
    "#l_flatten = flatten(l_pool4, scope=\"flatten\")\n",
    "\n",
    "## RNN\n",
    "# define the cell of your RNN\n",
    "#shape_cell = tf.nn.rnn_cell.GRUCell(100)\n",
    "# run the RNN as outputs, state = tf.nn.dynamic_rnn(cell, ...)\n",
    "# given we run many-to-one we only care about the last state, so only\n",
    "# shape_state is defined\n",
    "#_, shape_state = tf.nn.dynamic_rnn(cell=shape_cell,\n",
    "#    inputs=tf.expand_dims(batch_norm(x_shape_pl), 2), dtype=tf.float32, scope=\"shape_rnn\")\n",
    "\n",
    "## COMBINE\n",
    "# use margin, shape and texture only\n",
    "features = tf.concat(concat_dim=1, values=[x_margin_pl, x_shape_pl, x_texture_pl], name=\"features\")\n",
    "# uncomment to use image only\n",
    "#features = l_flatten\n",
    "# uncomment to use margin, rnn_state on shape and texture only\n",
    "#features = tf.concat(concat_dim=1, values=[x_margin_pl, shape_state, x_texture_pl], name=\"features\")\n",
    "features = batch_norm(features, scope='features_bn')\n",
    "#l2 = fully_connected(features, num_outputs=256, activation_fn=relu,\n",
    "#                     normalizer_fn=batch_norm, scope=\"l2\")\n",
    "#l2 = dropout(l2, is_training=is_training_pl, scope=\"l2_dropout\")\n",
    "y = fully_connected(features, NUM_CLASSES, activation_fn=softmax, scope=\"y\")\n",
    "\n",
    "# add TensorBoard summaries for all variables\n",
    "tf.contrib.layers.summarize_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_image_pl, (?, 128, 128, 1)\n",
      "x_margin_pl, (?, 64)\n",
      "x_shape_pl, (?, 64)\n",
      "x_texture_pl, (?, 64)\n",
      "features, (?, 192)\n",
      "y, (?, 99)\n"
     ]
    }
   ],
   "source": [
    "# PRINT NETWORK (good practice to also include outcommented code when using it)\n",
    "\n",
    "print \"x_image_pl,\", x_image_pl.get_shape()\n",
    "print \"x_margin_pl,\", x_margin_pl.get_shape()\n",
    "print \"x_shape_pl,\", x_shape_pl.get_shape()\n",
    "print \"x_texture_pl,\", x_texture_pl.get_shape()\n",
    "print \"features,\", features.get_shape()\n",
    "print \"y,\", y.get_shape()\n",
    "\n",
    "# for the MLP\n",
    "#print \"l2,\", l2.get_shape()\n",
    "# for the RNN\n",
    "#print \"shape_state,\", shape_state.get_shape()\n",
    "# for the CNN\n",
    "#print \"l_conv1_a,\", l_conv1_a.get_shape()\n",
    "#...\n",
    "#print \"l_pool4,\", l_pool4.get_shape()\n",
    "#print \"l_flatten,\", l_flatten.get_shape()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'ScalarSummary_4:0' shape=() dtype=string>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clip_norm = 1\n",
    "# y_ is a placeholder variable taking on the value of the target batch.\n",
    "ts_pl = tf.placeholder(tf.float32, [None, NUM_CLASSES], name=\"targets_pl\")\n",
    "lr_pl = tf.placeholder(tf.float32, [], name=\"learning_rate_pl\")\n",
    "\n",
    "def loss_and_acc(preds):\n",
    "    # computing cross entropy per sample\n",
    "    cross_entropy = -tf.reduce_sum(ts_pl * tf.log(preds+1e-10), reduction_indices=[1])\n",
    "    # averaging over samples\n",
    "    loss = tf.reduce_mean(cross_entropy)\n",
    "    # if you want regularization\n",
    "    #reg_scale = 0.0001\n",
    "    #regularize = tf.contrib.layers.l2_regularizer(reg_scale)\n",
    "    #params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n",
    "    #reg_term = sum([regularize(param) for param in params])\n",
    "    #loss += reg_term\n",
    "    # calculate accuracy\n",
    "    argmax_y = tf.to_int32(tf.argmax(preds, dimension=1))\n",
    "    argmax_t = tf.to_int32(tf.argmax(ts_pl, dimension=1))\n",
    "    correct = tf.to_float(tf.equal(argmax_y, argmax_t))\n",
    "    accuracy = tf.reduce_mean(correct)\n",
    "    return loss, accuracy, argmax_y\n",
    "\n",
    "# loss, accuracy and prediction\n",
    "loss, accuracy, prediction = loss_and_acc(y)\n",
    "\n",
    "loss_valid = loss\n",
    "accuracy_valid = accuracy\n",
    "loss_valid, accuracy_valid, _ = loss_and_acc(y)\n",
    "\n",
    "# defining our optimizer\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=0.001)\n",
    "\n",
    "# applying the gradients\n",
    "grads_and_vars = optimizer.compute_gradients(loss)\n",
    "gradients, variables = zip(*grads_and_vars)  # unzip list of tuples\n",
    "clipped_gradients, global_norm = (\n",
    "    tf.clip_by_global_norm(gradients, clip_norm) )\n",
    "clipped_grads_and_vars = zip(clipped_gradients, variables)\n",
    "\n",
    "# make training op for applying the gradients\n",
    "train_op = optimizer.apply_gradients(clipped_grads_and_vars)\n",
    "\n",
    "# make tensorboard summeries\n",
    "tf.scalar_summary('train/global gradient norm', global_norm)\n",
    "tf.scalar_summary('train/loss', loss)\n",
    "tf.scalar_summary('train/accuracy', accuracy)\n",
    "tf.scalar_summary('validation/loss', loss_valid)\n",
    "tf.scalar_summary('validation/accuracy', accuracy_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y (45, 99)\n"
     ]
    }
   ],
   "source": [
    "#Test the forward pass\n",
    "_img_shape = tuple([45]+list(IMAGE_SHAPE))\n",
    "_feature_shape = (45, NUM_FEATURES)\n",
    "_x_image = np.random.normal(0, 1, _img_shape).astype('float32') #dummy data\n",
    "_x_margin = np.random.normal(0, 1, _feature_shape).astype('float32')\n",
    "_x_shape = np.random.normal(0, 1, _feature_shape).astype('float32')\n",
    "_x_texture = np.random.normal(0, 1, _feature_shape).astype('float32')\n",
    "\n",
    "# restricting memory usage, TensorFlow is greedy and will use all memory otherwise\n",
    "gpu_opts = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)\n",
    "# initialize the Session\n",
    "sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_opts))\n",
    "# test the forward pass\n",
    "sess.run(tf.initialize_all_variables())\n",
    "feed_dict = {x_image_pl: _x_image,\n",
    "             x_margin_pl: _x_margin,\n",
    "             x_shape_pl: _x_shape,\n",
    "             x_texture_pl: _x_texture,\n",
    "             is_training_pl: False}\n",
    "res_forward_pass = sess.run(fetches=[y], feed_dict=feed_dict)\n",
    "print \"y\", res_forward_pass[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initiating batch generator\n",
      "batch generator initiated ...\n",
      "\ttrain_loss \ttrain_acc \tvalid_loss \tvalid_acc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:21: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\t  4.87\t\t  1.6\t\t  4.03\t\t  1.0\n",
      "100:\t  3.74\t\t  29.3\t\t  2.43\t\t  47.3\n",
      "200:\t  2.26\t\t  83.3\t\t  1.51\t\t  76.6\n",
      "300:\t  1.39\t\t  96.5\t\t  1.00\t\t  80.2\n",
      "400:\t  0.92\t\t  98.8\t\t  0.72\t\t  80.9\n",
      "500:\t  0.64\t\t  99.4\t\t  0.55\t\t  81.9\n",
      "600:\t  0.47\t\t  99.6\t\t  0.44\t\t  82.4\n",
      "700:\t  0.37\t\t  99.8\t\t  0.36\t\t  82.4\n",
      "800:\t  0.29\t\t  99.8\t\t  0.31\t\t  83.4\n",
      "900:\t  0.24\t\t  99.9\t\t  0.27\t\t  82.4\n",
      "1000:\t  0.20\t\t  99.9\t\t  0.24\t\t  83.4\n",
      "1100:\t  0.17\t\t  99.9\t\t  0.22\t\t  84.0\n",
      "1200:\t  0.15\t\t  99.9\t\t  0.20\t\t  84.0\n",
      "1300:\t  0.13\t\t  99.9\t\t  0.18\t\t  84.0\n",
      "1400:\t  0.11\t\t  99.9\t\t  0.17\t\t  84.0\n",
      "1500:\t  0.10\t\t  100.0\t\t  0.15\t\t  84.0\n",
      "1600:\t  0.09\t\t  100.0\t\t  0.14\t\t  84.0\n",
      "1700:\t  0.08\t\t  100.0\t\t  0.13\t\t  84.0\n",
      "1800:\t  0.07\t\t  100.0\t\t  0.13\t\t  84.0\n",
      "1900:\t  0.06\t\t  100.0\t\t  0.12\t\t  84.0\n",
      "2000:\t  0.06\t\t  100.0\t\t  0.11\t\t  84.0\n",
      "2100:\t  0.05\t\t  100.0\t\t  0.11\t\t  84.0\n",
      "2200:\t  0.05\t\t  100.0\t\t  0.10\t\t  84.0\n",
      "2300:\t  0.05\t\t  100.0\t\t  0.10\t\t  84.0\n",
      "2400:\t  0.04\t\t  100.0\t\t  0.09\t\t  84.0\n",
      "2500:\t  0.04\t\t  100.0\t\t  0.09\t\t  84.0\n",
      "2600:\t  0.04\t\t  100.0\t\t  0.08\t\t  84.0\n",
      "2700:\t  0.03\t\t  100.0\t\t  0.08\t\t  84.0\n",
      "2800:\t  0.03\t\t  100.0\t\t  0.08\t\t  84.0\n",
      "2900:\t  0.03\t\t  100.0\t\t  0.08\t\t  84.0\n",
      "3000:\t  0.03\t\t  100.0\t\t  0.07\t\t  84.0\n",
      "3100:\t  0.03\t\t  100.0\t\t  0.07\t\t  84.0\n",
      "3200:\t  0.02\t\t  100.0\t\t  0.07\t\t  84.0\n",
      "3300:\t  0.02\t\t  100.0\t\t  0.07\t\t  84.0\n",
      "3400:\t  0.02\t\t  100.0\t\t  0.06\t\t  84.0\n",
      "3500:\t  0.02\t\t  100.0\t\t  0.06\t\t  84.0\n",
      "3600:\t  0.02\t\t  100.0\t\t  0.06\t\t  84.0\n",
      "3700:\t  0.02\t\t  100.0\t\t  0.06\t\t  84.0\n",
      "3800:\t  0.02\t\t  100.0\t\t  0.06\t\t  84.0\n",
      "3900:\t  0.02\t\t  100.0\t\t  0.05\t\t  84.0\n",
      "4000:\t  0.01\t\t  100.0\t\t  0.05\t\t  84.0\n",
      "4100:\t  0.01\t\t  100.0\t\t  0.05\t\t  84.0\n",
      "4200:\t  0.01\t\t  100.0\t\t  0.05\t\t  84.0\n",
      "4300:\t  0.01\t\t  100.0\t\t  0.05\t\t  84.0\n",
      "4400:\t  0.01\t\t  100.0\t\t  0.05\t\t  84.0\n",
      "4500:\t  0.01\t\t  100.0\t\t  0.05\t\t  84.0\n",
      "4600:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "4700:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "4800:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "4900:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "5000:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "5100:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "5200:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "5300:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "5400:\t  0.01\t\t  100.0\t\t  0.04\t\t  84.0\n",
      "5500:\t  0.01\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "5600:\t  0.01\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "5700:\t  0.01\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "5800:\t  0.01\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "5900:\t  0.01\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6000:\t  0.01\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6100:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6200:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6300:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6400:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6500:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6600:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6700:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6800:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "6900:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "7000:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "7100:\t  0.00\t\t  100.0\t\t  0.03\t\t  84.0\n",
      "7200:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "7300:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "7400:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "7500:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "7600:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "7700:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "7800:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "7900:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8000:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8100:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8200:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8300:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8400:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8500:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8600:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8700:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8800:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "8900:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9000:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9100:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9200:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9300:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9400:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9500:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9600:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9700:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9800:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n",
      "9900:\t  0.00\t\t  100.0\t\t  0.02\t\t  84.0\n"
     ]
    }
   ],
   "source": [
    "#Training Loop\n",
    "BATCH_SIZE = 64\n",
    "ITERATIONS = 1e4\n",
    "LOG_FREQ = 10\n",
    "VALIDATION_SIZE = 0.1 # 0.1 is ~ 100 samples for valition\n",
    "SEED = 42\n",
    "DROPOUT = False\n",
    "LEARNING_RATE = 0.0005\n",
    "VALID_EVERY = 100\n",
    "\n",
    "batch_gen = batch_generator(data, batch_size=BATCH_SIZE, num_classes=NUM_CLASSES,\n",
    "                            num_iterations=ITERATIONS, seed=SEED, val_size=VALIDATION_SIZE)\n",
    "\n",
    "# setup and write summaries\n",
    "timestamp = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "summaries_path = \"tensorboard/%s/logs\" % (timestamp)\n",
    "summaries = tf.merge_all_summaries()\n",
    "summarywriter = tf.train.SummaryWriter(summaries_path, sess.graph)\n",
    "\n",
    "train_loss = []\n",
    "train_acc = []\n",
    "print \"\\ttrain_loss \\ttrain_acc \\tvalid_loss \\tvalid_acc\"\n",
    "for i, batch_train in enumerate(batch_gen.gen_train()):\n",
    "    if i>=ITERATIONS:\n",
    "        break\n",
    "    fetches_train = [train_op, loss, accuracy, summaries]\n",
    "    feed_dict_train = {\n",
    "        x_image_pl: batch_train['images'],\n",
    "        x_margin_pl: batch_train['margins'],\n",
    "        x_shape_pl: batch_train['shapes'],\n",
    "        x_texture_pl: batch_train['textures'],\n",
    "        ts_pl: batch_train['ts'],\n",
    "        is_training_pl: DROPOUT,\n",
    "        lr_pl: LEARNING_RATE,\n",
    "        \n",
    "    }\n",
    "    res_train = sess.run(fetches=fetches_train, feed_dict=feed_dict_train)\n",
    "    if i % LOG_FREQ == 0:\n",
    "        summarywriter.add_summary(res_train[3], i)\n",
    "    train_loss.append(res_train[1])\n",
    "    train_acc.append(res_train[2])\n",
    "    \n",
    "    # validate\n",
    "    if i % VALID_EVERY == 0:\n",
    "        cur_acc = 0\n",
    "        cur_loss = 0\n",
    "        tot_num = 0\n",
    "        # batch validation\n",
    "        for batch_valid, num in batch_gen.gen_valid():\n",
    "            # fetches and feed_dict for validation\n",
    "            fetches_valid = [loss_valid, accuracy_valid, summaries]\n",
    "            feed_dict_valid = {\n",
    "                x_image_pl: batch_valid['images'],\n",
    "                x_margin_pl: batch_valid['margins'],\n",
    "                x_shape_pl: batch_valid['shapes'],\n",
    "                x_texture_pl: batch_valid['textures'],\n",
    "                ts_pl: batch_valid['ts'],\n",
    "                is_training_pl: False,\n",
    "            }\n",
    "            # run validation\n",
    "            res_valid = sess.run(fetches=fetches_valid, feed_dict=feed_dict_valid)\n",
    "            # tensorboard and costs\n",
    "            summarywriter.add_summary(res_valid[2], i)\n",
    "            cur_loss += res_valid[0]*num\n",
    "            cur_acc += res_valid[1]*num\n",
    "            tot_num += num\n",
    "        valid_loss = cur_loss / float(tot_num)\n",
    "        valid_acc = (cur_acc / float(tot_num)) * 100\n",
    "        train_loss = sum(train_loss) / float(len(train_loss))\n",
    "        train_acc = sum(train_acc) / float(len(train_acc)) * 100\n",
    "        print \"%d:\\t  %.2f\\t\\t  %.1f\\t\\t  %.2f\\t\\t  %.1f\" % (i, train_loss, train_acc, valid_loss, valid_acc)\n",
    "        train_loss = []\n",
    "        train_acc = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorBoard\n",
    "\n",
    "The code above has TensorBoard tracking histograms of all layers, train gradient norm, accuracy, loss, validation accuracy and loss.\n",
    "\n",
    "The TensorBoard summaries are written to the tensorboard folder. To enable TensorBoard start a new Docker instance (in similar fashion to the one you are running) forwarding port 6006, as below\n",
    "\n",
    "```\n",
    "> docker run -p 6006:6006 -v $PATH\\_TO\\_FOLDER/tensorflow_tutorial:/mnt/myproject -it alrojo/tf-sklearn-cpu\n",
    "> cd mnt/myproject/lab6_Kaggle\n",
    "> tensorboard --logdir=tensorboard\n",
    "```\n",
    "Now open a browser window and connect to `localhost:6006`, here you should find the TensorBoard of your current run.\n",
    "\n",
    "NOTE: when using docker toolbox on windows the port will probably not bind to local host, instead you must find the port it binds to by typing the following in your docker prompt\n",
    "\n",
    ">docker-machine ip\n",
    "\n",
    "this should give you an ip that you can replace with localhost.\n",
    "\n",
    "Note: CTRL+c when running TensorBoard might cause the program to halt. To terminate it just exit the terminal, open a new terminal, type\n",
    "\n",
    "```\n",
    "> docker ps\n",
    "```\n",
    "\n",
    "if your output looks something like this:\n",
    "```\n",
    "CONTAINER ID        IMAGE                   COMMAND             CREATED             STATUS              PORTS                              NAMES\n",
    "e62ed10401cd        alrojo/tf-sklearn-cpu   \"/bin/bash\"         16 minutes ago      Up 16 minutes       0.0.0.0:6006->6006/tcp, 8888/tcp   gigantic_euler\n",
    "7846236dbf57        alrojo/tf-sklearn-cpu   \"/bin/bash\"         About an hour ago   Up About an hour    6006/tcp, 0.0.0.0:8888->8888/tcp   high_gates\n",
    "\n",
    "```\n",
    "\n",
    "That means you still have the docker running and you need to shut it down if you want to run a new docker with the same port forwarding. To do, use either the `CONTAINER ID` or the `NAMES` and run:\n",
    "\n",
    "```\n",
    "> docker kill gigantic_euler\n",
    "```\n",
    "Note: In my case the `NAMES` was `gigantic_euler`, yours might be different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission to Kaggle\n",
    "\n",
    "First we have to make testset predictions, then we have to place it in the submission file and the upload to kaggle for our score! You can upload at max 5 submissions a day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GET PREDICTIONS\n",
    "# containers to collect ids and predictions\n",
    "ids_test = []\n",
    "preds_test = []\n",
    "# run like with validation\n",
    "for batch_test, num in batch_gen.gen_test():\n",
    "    # fetching for test we only need y\n",
    "    fetches_test = [y]\n",
    "    # same as validation, but no batch['ts']\n",
    "    feed_dict_test = {\n",
    "        x_image_pl: batch_test['images'],\n",
    "        x_margin_pl: batch_test['margins'],\n",
    "        x_shape_pl: batch_test['shapes'],\n",
    "        x_texture_pl: batch_test['textures'],\n",
    "        is_training_pl: False\n",
    "    }\n",
    "    # get the result\n",
    "    res_test = sess.run(fetches=fetches_test, feed_dict=feed_dict_test)\n",
    "    y_out = res_test[0]\n",
    "    ids_test.append(batch_test['ids'])\n",
    "    if num!=len(y_out):\n",
    "        # in case of the last batch, num will be less than batch_size\n",
    "        y_out = y_out[:num]\n",
    "    preds_test.append(y_out)\n",
    "# concatenate it all, to form one list/array\n",
    "ids_test = list(itertools.chain.from_iterable(ids_test))\n",
    "preds_test = np.concatenate(preds_test, axis=0)\n",
    "assert len(ids_test) == len(preds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Acer_Capillipes</th>\n",
       "      <th>Acer_Circinatum</th>\n",
       "      <th>Acer_Mono</th>\n",
       "      <th>Acer_Opalus</th>\n",
       "      <th>Acer_Palmatum</th>\n",
       "      <th>Acer_Pictum</th>\n",
       "      <th>Acer_Platanoids</th>\n",
       "      <th>Acer_Rubrum</th>\n",
       "      <th>Acer_Rufinerve</th>\n",
       "      <th>...</th>\n",
       "      <th>Salix_Fragilis</th>\n",
       "      <th>Salix_Intergra</th>\n",
       "      <th>Sorbus_Aria</th>\n",
       "      <th>Tilia_Oliveri</th>\n",
       "      <th>Tilia_Platyphyllos</th>\n",
       "      <th>Tilia_Tomentosa</th>\n",
       "      <th>Ulmus_Bergmanniana</th>\n",
       "      <th>Viburnum_Tinus</th>\n",
       "      <th>Viburnum_x_Rhytidophylloides</th>\n",
       "      <th>Zelkova_Serrata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>5.564099e-09</td>\n",
       "      <td>3.995893e-09</td>\n",
       "      <td>3.215826e-10</td>\n",
       "      <td>3.579184e-05</td>\n",
       "      <td>1.534015e-09</td>\n",
       "      <td>3.922370e-11</td>\n",
       "      <td>3.727405e-08</td>\n",
       "      <td>2.401681e-09</td>\n",
       "      <td>4.123072e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>6.463605e-10</td>\n",
       "      <td>5.186318e-06</td>\n",
       "      <td>1.572735e-08</td>\n",
       "      <td>2.441709e-09</td>\n",
       "      <td>4.433265e-07</td>\n",
       "      <td>1.181075e-09</td>\n",
       "      <td>2.073514e-11</td>\n",
       "      <td>3.742596e-10</td>\n",
       "      <td>1.466789e-09</td>\n",
       "      <td>6.290616e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4.234810e-09</td>\n",
       "      <td>3.445131e-08</td>\n",
       "      <td>1.008688e-07</td>\n",
       "      <td>1.249469e-06</td>\n",
       "      <td>1.099376e-07</td>\n",
       "      <td>3.611913e-08</td>\n",
       "      <td>2.375303e-06</td>\n",
       "      <td>1.860793e-08</td>\n",
       "      <td>5.416962e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>2.938037e-09</td>\n",
       "      <td>3.328846e-08</td>\n",
       "      <td>9.049756e-09</td>\n",
       "      <td>1.432459e-08</td>\n",
       "      <td>4.233897e-09</td>\n",
       "      <td>2.621590e-06</td>\n",
       "      <td>1.648111e-09</td>\n",
       "      <td>1.533108e-06</td>\n",
       "      <td>2.634925e-09</td>\n",
       "      <td>6.199036e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>9.157161e-08</td>\n",
       "      <td>9.980009e-01</td>\n",
       "      <td>1.720776e-07</td>\n",
       "      <td>3.295709e-07</td>\n",
       "      <td>1.197206e-03</td>\n",
       "      <td>1.378777e-06</td>\n",
       "      <td>2.659681e-08</td>\n",
       "      <td>3.054111e-05</td>\n",
       "      <td>2.208277e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>3.714409e-07</td>\n",
       "      <td>1.045037e-07</td>\n",
       "      <td>1.498809e-07</td>\n",
       "      <td>2.413588e-08</td>\n",
       "      <td>1.329892e-10</td>\n",
       "      <td>8.576847e-08</td>\n",
       "      <td>2.674542e-07</td>\n",
       "      <td>1.345941e-09</td>\n",
       "      <td>4.951499e-09</td>\n",
       "      <td>2.277303e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>3.137234e-10</td>\n",
       "      <td>7.157261e-05</td>\n",
       "      <td>4.558301e-08</td>\n",
       "      <td>2.259347e-09</td>\n",
       "      <td>2.805615e-07</td>\n",
       "      <td>1.204050e-09</td>\n",
       "      <td>2.721501e-05</td>\n",
       "      <td>4.671056e-08</td>\n",
       "      <td>7.129248e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.857425e-07</td>\n",
       "      <td>2.992218e-09</td>\n",
       "      <td>2.208566e-06</td>\n",
       "      <td>3.269031e-08</td>\n",
       "      <td>2.777161e-07</td>\n",
       "      <td>4.933698e-06</td>\n",
       "      <td>2.635645e-03</td>\n",
       "      <td>1.132239e-07</td>\n",
       "      <td>6.438043e-07</td>\n",
       "      <td>3.766651e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>5.795870e-08</td>\n",
       "      <td>1.015203e-07</td>\n",
       "      <td>1.287603e-10</td>\n",
       "      <td>9.806435e-10</td>\n",
       "      <td>3.148348e-08</td>\n",
       "      <td>5.977766e-11</td>\n",
       "      <td>3.276833e-07</td>\n",
       "      <td>8.885695e-09</td>\n",
       "      <td>2.571370e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>5.008176e-07</td>\n",
       "      <td>1.483564e-09</td>\n",
       "      <td>8.166779e-06</td>\n",
       "      <td>4.322440e-08</td>\n",
       "      <td>1.759922e-05</td>\n",
       "      <td>3.189810e-06</td>\n",
       "      <td>7.981434e-05</td>\n",
       "      <td>2.553334e-07</td>\n",
       "      <td>3.665428e-08</td>\n",
       "      <td>1.496972e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>2.953344e-06</td>\n",
       "      <td>6.158201e-07</td>\n",
       "      <td>9.883947e-07</td>\n",
       "      <td>9.215780e-01</td>\n",
       "      <td>1.191772e-06</td>\n",
       "      <td>1.115861e-07</td>\n",
       "      <td>1.191132e-05</td>\n",
       "      <td>2.849477e-04</td>\n",
       "      <td>2.764408e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.062067e-04</td>\n",
       "      <td>2.890773e-05</td>\n",
       "      <td>2.903316e-05</td>\n",
       "      <td>6.455876e-05</td>\n",
       "      <td>2.516816e-04</td>\n",
       "      <td>2.165608e-04</td>\n",
       "      <td>2.039107e-06</td>\n",
       "      <td>1.924955e-06</td>\n",
       "      <td>2.673305e-04</td>\n",
       "      <td>1.474009e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19</td>\n",
       "      <td>2.618619e-06</td>\n",
       "      <td>1.213384e-06</td>\n",
       "      <td>1.164345e-06</td>\n",
       "      <td>9.973845e-01</td>\n",
       "      <td>1.977085e-07</td>\n",
       "      <td>3.813481e-08</td>\n",
       "      <td>1.879613e-06</td>\n",
       "      <td>5.791098e-07</td>\n",
       "      <td>9.794209e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1.361041e-07</td>\n",
       "      <td>5.301088e-06</td>\n",
       "      <td>4.502169e-06</td>\n",
       "      <td>1.781421e-06</td>\n",
       "      <td>2.038559e-05</td>\n",
       "      <td>1.202596e-04</td>\n",
       "      <td>2.928770e-08</td>\n",
       "      <td>2.013095e-06</td>\n",
       "      <td>8.148651e-06</td>\n",
       "      <td>4.414580e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23</td>\n",
       "      <td>1.599135e-10</td>\n",
       "      <td>5.748869e-06</td>\n",
       "      <td>1.504531e-06</td>\n",
       "      <td>2.391018e-06</td>\n",
       "      <td>1.457821e-07</td>\n",
       "      <td>9.795280e-05</td>\n",
       "      <td>2.314170e-06</td>\n",
       "      <td>8.684391e-10</td>\n",
       "      <td>7.397863e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>4.542820e-09</td>\n",
       "      <td>1.274733e-07</td>\n",
       "      <td>3.446437e-09</td>\n",
       "      <td>2.159990e-09</td>\n",
       "      <td>4.277613e-07</td>\n",
       "      <td>1.438084e-09</td>\n",
       "      <td>2.114243e-10</td>\n",
       "      <td>2.195948e-07</td>\n",
       "      <td>5.251225e-08</td>\n",
       "      <td>1.933370e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>9.315323e-07</td>\n",
       "      <td>1.018916e-07</td>\n",
       "      <td>1.891389e-08</td>\n",
       "      <td>9.358384e-11</td>\n",
       "      <td>5.781123e-08</td>\n",
       "      <td>1.202895e-06</td>\n",
       "      <td>1.173221e-06</td>\n",
       "      <td>2.271810e-08</td>\n",
       "      <td>7.574131e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>8.600087e-09</td>\n",
       "      <td>1.222477e-09</td>\n",
       "      <td>4.911558e-10</td>\n",
       "      <td>1.202762e-07</td>\n",
       "      <td>5.178366e-10</td>\n",
       "      <td>7.382057e-10</td>\n",
       "      <td>5.323598e-11</td>\n",
       "      <td>4.985075e-12</td>\n",
       "      <td>2.322560e-11</td>\n",
       "      <td>6.856449e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>1.210373e-04</td>\n",
       "      <td>2.806766e-05</td>\n",
       "      <td>1.315054e-07</td>\n",
       "      <td>8.721123e-08</td>\n",
       "      <td>2.161358e-07</td>\n",
       "      <td>3.098198e-08</td>\n",
       "      <td>2.940067e-06</td>\n",
       "      <td>3.875687e-05</td>\n",
       "      <td>9.994276e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>7.775509e-06</td>\n",
       "      <td>2.366486e-07</td>\n",
       "      <td>1.401758e-05</td>\n",
       "      <td>1.583462e-07</td>\n",
       "      <td>1.801642e-06</td>\n",
       "      <td>1.189907e-04</td>\n",
       "      <td>3.301221e-06</td>\n",
       "      <td>4.633716e-11</td>\n",
       "      <td>4.395520e-09</td>\n",
       "      <td>1.093372e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33</td>\n",
       "      <td>3.932919e-08</td>\n",
       "      <td>2.756033e-09</td>\n",
       "      <td>5.856367e-06</td>\n",
       "      <td>3.652184e-06</td>\n",
       "      <td>1.089351e-07</td>\n",
       "      <td>1.034257e-06</td>\n",
       "      <td>1.277500e-07</td>\n",
       "      <td>1.355123e-09</td>\n",
       "      <td>1.215952e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>5.391952e-07</td>\n",
       "      <td>1.112631e-07</td>\n",
       "      <td>4.638495e-07</td>\n",
       "      <td>2.243190e-05</td>\n",
       "      <td>1.238102e-07</td>\n",
       "      <td>2.200176e-11</td>\n",
       "      <td>1.763415e-06</td>\n",
       "      <td>6.877352e-04</td>\n",
       "      <td>1.250015e-03</td>\n",
       "      <td>1.819961e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36</td>\n",
       "      <td>1.406331e-05</td>\n",
       "      <td>3.388038e-09</td>\n",
       "      <td>2.383083e-08</td>\n",
       "      <td>7.584937e-10</td>\n",
       "      <td>1.929676e-07</td>\n",
       "      <td>1.492650e-08</td>\n",
       "      <td>1.542286e-09</td>\n",
       "      <td>4.010165e-08</td>\n",
       "      <td>3.703175e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.573415e-10</td>\n",
       "      <td>1.088290e-10</td>\n",
       "      <td>5.676565e-07</td>\n",
       "      <td>4.219507e-09</td>\n",
       "      <td>4.645312e-08</td>\n",
       "      <td>9.208544e-11</td>\n",
       "      <td>1.955501e-09</td>\n",
       "      <td>7.486872e-09</td>\n",
       "      <td>3.623627e-09</td>\n",
       "      <td>2.566271e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39</td>\n",
       "      <td>1.572562e-06</td>\n",
       "      <td>1.396278e-11</td>\n",
       "      <td>1.645762e-07</td>\n",
       "      <td>2.428697e-07</td>\n",
       "      <td>4.341697e-10</td>\n",
       "      <td>7.826118e-09</td>\n",
       "      <td>1.135842e-10</td>\n",
       "      <td>1.228455e-09</td>\n",
       "      <td>1.608048e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>4.938538e-08</td>\n",
       "      <td>2.732019e-06</td>\n",
       "      <td>4.861549e-11</td>\n",
       "      <td>6.666294e-06</td>\n",
       "      <td>5.054454e-09</td>\n",
       "      <td>3.875538e-10</td>\n",
       "      <td>7.182437e-11</td>\n",
       "      <td>4.052775e-07</td>\n",
       "      <td>5.120274e-08</td>\n",
       "      <td>2.668129e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>41</td>\n",
       "      <td>9.944187e-08</td>\n",
       "      <td>5.705782e-09</td>\n",
       "      <td>5.300804e-05</td>\n",
       "      <td>7.948324e-07</td>\n",
       "      <td>5.958057e-09</td>\n",
       "      <td>2.822317e-08</td>\n",
       "      <td>5.388822e-08</td>\n",
       "      <td>2.926009e-08</td>\n",
       "      <td>3.137843e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>2.338334e-07</td>\n",
       "      <td>1.676785e-07</td>\n",
       "      <td>6.979811e-09</td>\n",
       "      <td>1.269386e-05</td>\n",
       "      <td>3.549302e-08</td>\n",
       "      <td>1.344781e-04</td>\n",
       "      <td>2.228624e-09</td>\n",
       "      <td>3.995197e-11</td>\n",
       "      <td>2.528558e-08</td>\n",
       "      <td>1.299757e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44</td>\n",
       "      <td>2.618808e-11</td>\n",
       "      <td>8.096802e-11</td>\n",
       "      <td>1.504561e-11</td>\n",
       "      <td>1.771722e-06</td>\n",
       "      <td>4.521733e-09</td>\n",
       "      <td>2.988362e-10</td>\n",
       "      <td>7.441791e-10</td>\n",
       "      <td>1.249217e-08</td>\n",
       "      <td>4.900608e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>3.347493e-08</td>\n",
       "      <td>1.993373e-09</td>\n",
       "      <td>2.955677e-10</td>\n",
       "      <td>5.435498e-08</td>\n",
       "      <td>4.676059e-08</td>\n",
       "      <td>1.454506e-08</td>\n",
       "      <td>1.280033e-11</td>\n",
       "      <td>9.011586e-06</td>\n",
       "      <td>1.294304e-11</td>\n",
       "      <td>1.260969e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>46</td>\n",
       "      <td>1.986400e-08</td>\n",
       "      <td>6.700429e-07</td>\n",
       "      <td>6.823167e-07</td>\n",
       "      <td>1.535088e-04</td>\n",
       "      <td>2.241645e-08</td>\n",
       "      <td>2.329560e-08</td>\n",
       "      <td>7.488351e-06</td>\n",
       "      <td>1.269853e-09</td>\n",
       "      <td>1.521007e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.012804e-08</td>\n",
       "      <td>5.993997e-09</td>\n",
       "      <td>3.414696e-04</td>\n",
       "      <td>2.881139e-06</td>\n",
       "      <td>7.856298e-06</td>\n",
       "      <td>1.338125e-06</td>\n",
       "      <td>1.577276e-05</td>\n",
       "      <td>5.722030e-06</td>\n",
       "      <td>3.518631e-06</td>\n",
       "      <td>8.212031e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47</td>\n",
       "      <td>1.783843e-07</td>\n",
       "      <td>2.013781e-07</td>\n",
       "      <td>5.944322e-05</td>\n",
       "      <td>4.065487e-09</td>\n",
       "      <td>5.624620e-08</td>\n",
       "      <td>6.533107e-07</td>\n",
       "      <td>1.663459e-04</td>\n",
       "      <td>2.999145e-08</td>\n",
       "      <td>8.789836e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>7.026609e-09</td>\n",
       "      <td>9.431191e-07</td>\n",
       "      <td>6.480544e-08</td>\n",
       "      <td>3.523070e-07</td>\n",
       "      <td>2.817687e-09</td>\n",
       "      <td>1.409496e-09</td>\n",
       "      <td>5.072697e-11</td>\n",
       "      <td>3.400663e-11</td>\n",
       "      <td>7.061470e-10</td>\n",
       "      <td>1.770474e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>51</td>\n",
       "      <td>1.308695e-07</td>\n",
       "      <td>1.292476e-07</td>\n",
       "      <td>3.761577e-04</td>\n",
       "      <td>1.588583e-07</td>\n",
       "      <td>3.841262e-07</td>\n",
       "      <td>5.976147e-06</td>\n",
       "      <td>4.282566e-07</td>\n",
       "      <td>6.127383e-05</td>\n",
       "      <td>6.198897e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>5.834585e-07</td>\n",
       "      <td>2.037118e-05</td>\n",
       "      <td>4.348942e-10</td>\n",
       "      <td>7.464701e-06</td>\n",
       "      <td>1.843490e-09</td>\n",
       "      <td>1.038107e-06</td>\n",
       "      <td>2.044559e-09</td>\n",
       "      <td>1.201631e-09</td>\n",
       "      <td>4.101938e-07</td>\n",
       "      <td>1.466581e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>52</td>\n",
       "      <td>3.626835e-07</td>\n",
       "      <td>1.208955e-06</td>\n",
       "      <td>3.527771e-08</td>\n",
       "      <td>4.542050e-08</td>\n",
       "      <td>5.561457e-07</td>\n",
       "      <td>3.110689e-08</td>\n",
       "      <td>2.573891e-06</td>\n",
       "      <td>6.797789e-04</td>\n",
       "      <td>4.081283e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.629303e-05</td>\n",
       "      <td>1.626204e-08</td>\n",
       "      <td>6.951186e-07</td>\n",
       "      <td>1.856436e-04</td>\n",
       "      <td>1.960887e-07</td>\n",
       "      <td>1.284283e-05</td>\n",
       "      <td>2.860258e-05</td>\n",
       "      <td>1.947592e-08</td>\n",
       "      <td>1.219842e-08</td>\n",
       "      <td>1.370336e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>53</td>\n",
       "      <td>1.544654e-06</td>\n",
       "      <td>1.428886e-08</td>\n",
       "      <td>1.318026e-10</td>\n",
       "      <td>4.026909e-09</td>\n",
       "      <td>4.878647e-07</td>\n",
       "      <td>1.790830e-05</td>\n",
       "      <td>3.388427e-07</td>\n",
       "      <td>9.453548e-10</td>\n",
       "      <td>8.161784e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>3.031005e-08</td>\n",
       "      <td>3.323287e-06</td>\n",
       "      <td>2.597561e-11</td>\n",
       "      <td>1.914445e-07</td>\n",
       "      <td>2.502503e-08</td>\n",
       "      <td>1.631993e-10</td>\n",
       "      <td>1.975647e-09</td>\n",
       "      <td>1.277459e-11</td>\n",
       "      <td>1.096317e-10</td>\n",
       "      <td>3.585779e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>57</td>\n",
       "      <td>1.678146e-10</td>\n",
       "      <td>4.611596e-12</td>\n",
       "      <td>2.955799e-08</td>\n",
       "      <td>6.005782e-12</td>\n",
       "      <td>7.919481e-10</td>\n",
       "      <td>1.440238e-10</td>\n",
       "      <td>2.978474e-13</td>\n",
       "      <td>1.056984e-12</td>\n",
       "      <td>3.973030e-14</td>\n",
       "      <td>...</td>\n",
       "      <td>4.533909e-10</td>\n",
       "      <td>1.249250e-06</td>\n",
       "      <td>9.606800e-14</td>\n",
       "      <td>2.562269e-12</td>\n",
       "      <td>2.523007e-08</td>\n",
       "      <td>5.376545e-14</td>\n",
       "      <td>1.050258e-12</td>\n",
       "      <td>4.435981e-05</td>\n",
       "      <td>3.083702e-09</td>\n",
       "      <td>2.289574e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>59</td>\n",
       "      <td>8.913486e-06</td>\n",
       "      <td>5.710220e-10</td>\n",
       "      <td>2.428150e-04</td>\n",
       "      <td>2.099650e-07</td>\n",
       "      <td>9.655992e-08</td>\n",
       "      <td>4.709331e-08</td>\n",
       "      <td>5.804229e-09</td>\n",
       "      <td>9.371228e-09</td>\n",
       "      <td>6.285596e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.369631e-08</td>\n",
       "      <td>8.120645e-06</td>\n",
       "      <td>7.053135e-09</td>\n",
       "      <td>1.008887e-06</td>\n",
       "      <td>8.984427e-07</td>\n",
       "      <td>5.350731e-08</td>\n",
       "      <td>6.599065e-10</td>\n",
       "      <td>7.458794e-05</td>\n",
       "      <td>1.273415e-05</td>\n",
       "      <td>4.774234e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>62</td>\n",
       "      <td>4.317981e-14</td>\n",
       "      <td>5.068797e-12</td>\n",
       "      <td>2.504857e-12</td>\n",
       "      <td>2.717345e-10</td>\n",
       "      <td>9.557354e-11</td>\n",
       "      <td>7.302641e-10</td>\n",
       "      <td>4.624729e-11</td>\n",
       "      <td>9.271926e-12</td>\n",
       "      <td>9.487337e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>6.298651e-08</td>\n",
       "      <td>1.481508e-12</td>\n",
       "      <td>2.760454e-13</td>\n",
       "      <td>2.509549e-11</td>\n",
       "      <td>2.007160e-13</td>\n",
       "      <td>3.998215e-13</td>\n",
       "      <td>2.141353e-12</td>\n",
       "      <td>2.007918e-10</td>\n",
       "      <td>7.583069e-13</td>\n",
       "      <td>2.055387e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>65</td>\n",
       "      <td>3.790941e-14</td>\n",
       "      <td>6.894957e-12</td>\n",
       "      <td>4.189668e-08</td>\n",
       "      <td>1.024114e-11</td>\n",
       "      <td>2.518811e-11</td>\n",
       "      <td>2.123407e-11</td>\n",
       "      <td>3.491687e-06</td>\n",
       "      <td>3.217698e-14</td>\n",
       "      <td>6.335027e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.069225e-09</td>\n",
       "      <td>6.288038e-10</td>\n",
       "      <td>3.415210e-09</td>\n",
       "      <td>2.757435e-10</td>\n",
       "      <td>9.946258e-07</td>\n",
       "      <td>7.224162e-11</td>\n",
       "      <td>1.365386e-06</td>\n",
       "      <td>3.676520e-05</td>\n",
       "      <td>7.906191e-05</td>\n",
       "      <td>1.038370e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>68</td>\n",
       "      <td>3.667399e-09</td>\n",
       "      <td>9.996654e-01</td>\n",
       "      <td>6.208755e-09</td>\n",
       "      <td>2.260363e-09</td>\n",
       "      <td>1.224216e-06</td>\n",
       "      <td>2.777808e-09</td>\n",
       "      <td>3.994571e-08</td>\n",
       "      <td>2.146493e-07</td>\n",
       "      <td>2.636226e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>3.232613e-08</td>\n",
       "      <td>6.490126e-08</td>\n",
       "      <td>1.237012e-08</td>\n",
       "      <td>1.898051e-09</td>\n",
       "      <td>3.874600e-10</td>\n",
       "      <td>5.196485e-08</td>\n",
       "      <td>5.991071e-07</td>\n",
       "      <td>2.160162e-10</td>\n",
       "      <td>2.052270e-09</td>\n",
       "      <td>7.264055e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>70</td>\n",
       "      <td>7.649607e-09</td>\n",
       "      <td>1.415651e-08</td>\n",
       "      <td>1.817908e-06</td>\n",
       "      <td>1.579273e-08</td>\n",
       "      <td>8.333610e-08</td>\n",
       "      <td>3.061456e-07</td>\n",
       "      <td>2.402332e-09</td>\n",
       "      <td>1.936109e-09</td>\n",
       "      <td>1.669763e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>2.172881e-09</td>\n",
       "      <td>1.132690e-07</td>\n",
       "      <td>3.614806e-08</td>\n",
       "      <td>6.683633e-09</td>\n",
       "      <td>2.806353e-07</td>\n",
       "      <td>1.614547e-10</td>\n",
       "      <td>2.447734e-08</td>\n",
       "      <td>1.755850e-05</td>\n",
       "      <td>5.479419e-07</td>\n",
       "      <td>8.682599e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>74</td>\n",
       "      <td>2.309302e-12</td>\n",
       "      <td>2.145764e-08</td>\n",
       "      <td>3.529617e-08</td>\n",
       "      <td>1.784948e-07</td>\n",
       "      <td>8.645630e-08</td>\n",
       "      <td>9.718477e-06</td>\n",
       "      <td>4.785320e-08</td>\n",
       "      <td>4.965027e-10</td>\n",
       "      <td>1.570151e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>1.350725e-07</td>\n",
       "      <td>3.096884e-09</td>\n",
       "      <td>6.011512e-10</td>\n",
       "      <td>1.921669e-07</td>\n",
       "      <td>3.913812e-09</td>\n",
       "      <td>1.665448e-09</td>\n",
       "      <td>2.717474e-09</td>\n",
       "      <td>6.935725e-06</td>\n",
       "      <td>4.779661e-09</td>\n",
       "      <td>1.495983e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>77</td>\n",
       "      <td>9.740345e-17</td>\n",
       "      <td>1.926140e-10</td>\n",
       "      <td>1.063341e-12</td>\n",
       "      <td>6.884135e-09</td>\n",
       "      <td>6.317468e-12</td>\n",
       "      <td>4.087843e-09</td>\n",
       "      <td>1.776700e-07</td>\n",
       "      <td>1.853469e-13</td>\n",
       "      <td>1.168438e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.070038e-11</td>\n",
       "      <td>1.733703e-13</td>\n",
       "      <td>2.093995e-13</td>\n",
       "      <td>6.106769e-13</td>\n",
       "      <td>2.351385e-13</td>\n",
       "      <td>1.308822e-10</td>\n",
       "      <td>5.477730e-12</td>\n",
       "      <td>8.137219e-12</td>\n",
       "      <td>1.805210e-12</td>\n",
       "      <td>1.451984e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>79</td>\n",
       "      <td>7.727279e-06</td>\n",
       "      <td>2.836519e-08</td>\n",
       "      <td>3.864822e-07</td>\n",
       "      <td>4.951246e-09</td>\n",
       "      <td>4.390390e-07</td>\n",
       "      <td>2.406830e-05</td>\n",
       "      <td>4.178570e-09</td>\n",
       "      <td>3.992743e-10</td>\n",
       "      <td>4.132396e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1.896494e-08</td>\n",
       "      <td>1.535780e-07</td>\n",
       "      <td>7.601535e-09</td>\n",
       "      <td>2.080706e-07</td>\n",
       "      <td>1.015040e-09</td>\n",
       "      <td>3.403680e-12</td>\n",
       "      <td>1.073434e-09</td>\n",
       "      <td>3.315712e-13</td>\n",
       "      <td>6.934154e-07</td>\n",
       "      <td>3.267973e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>86</td>\n",
       "      <td>1.078814e-09</td>\n",
       "      <td>4.471940e-10</td>\n",
       "      <td>1.269325e-07</td>\n",
       "      <td>9.037996e-10</td>\n",
       "      <td>7.564464e-09</td>\n",
       "      <td>7.925620e-08</td>\n",
       "      <td>5.192035e-10</td>\n",
       "      <td>5.829949e-11</td>\n",
       "      <td>1.029941e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>6.981262e-11</td>\n",
       "      <td>5.602633e-10</td>\n",
       "      <td>1.499131e-09</td>\n",
       "      <td>4.790459e-10</td>\n",
       "      <td>6.378123e-09</td>\n",
       "      <td>1.651472e-11</td>\n",
       "      <td>3.438645e-10</td>\n",
       "      <td>8.933652e-07</td>\n",
       "      <td>9.562712e-08</td>\n",
       "      <td>1.653262e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>1493</td>\n",
       "      <td>1.441340e-07</td>\n",
       "      <td>5.486910e-05</td>\n",
       "      <td>1.550863e-05</td>\n",
       "      <td>8.767483e-08</td>\n",
       "      <td>3.547741e-05</td>\n",
       "      <td>5.675403e-05</td>\n",
       "      <td>1.721817e-05</td>\n",
       "      <td>1.176765e-07</td>\n",
       "      <td>2.184806e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>2.107479e-05</td>\n",
       "      <td>3.124743e-07</td>\n",
       "      <td>9.709215e-05</td>\n",
       "      <td>1.624309e-04</td>\n",
       "      <td>8.408316e-07</td>\n",
       "      <td>8.668955e-09</td>\n",
       "      <td>5.247108e-04</td>\n",
       "      <td>5.594857e-05</td>\n",
       "      <td>3.266238e-05</td>\n",
       "      <td>6.672587e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>1495</td>\n",
       "      <td>4.566245e-10</td>\n",
       "      <td>8.251230e-10</td>\n",
       "      <td>9.165857e-07</td>\n",
       "      <td>8.792121e-10</td>\n",
       "      <td>1.188359e-08</td>\n",
       "      <td>1.452854e-08</td>\n",
       "      <td>5.592016e-09</td>\n",
       "      <td>7.139153e-07</td>\n",
       "      <td>5.120421e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>7.506396e-07</td>\n",
       "      <td>1.060539e-07</td>\n",
       "      <td>1.874987e-11</td>\n",
       "      <td>9.985044e-01</td>\n",
       "      <td>3.573243e-09</td>\n",
       "      <td>6.257854e-09</td>\n",
       "      <td>9.867460e-11</td>\n",
       "      <td>9.440727e-09</td>\n",
       "      <td>1.119859e-09</td>\n",
       "      <td>1.984872e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>1497</td>\n",
       "      <td>3.661469e-04</td>\n",
       "      <td>1.309526e-03</td>\n",
       "      <td>1.190103e-08</td>\n",
       "      <td>3.419827e-06</td>\n",
       "      <td>4.901422e-05</td>\n",
       "      <td>4.108013e-08</td>\n",
       "      <td>1.337763e-07</td>\n",
       "      <td>1.152306e-05</td>\n",
       "      <td>1.507953e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>3.208211e-05</td>\n",
       "      <td>1.657194e-07</td>\n",
       "      <td>5.789270e-05</td>\n",
       "      <td>1.669847e-06</td>\n",
       "      <td>7.731309e-04</td>\n",
       "      <td>8.956444e-05</td>\n",
       "      <td>4.984578e-04</td>\n",
       "      <td>9.657998e-08</td>\n",
       "      <td>1.158567e-07</td>\n",
       "      <td>2.675364e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>1498</td>\n",
       "      <td>2.372804e-11</td>\n",
       "      <td>4.854597e-09</td>\n",
       "      <td>8.507824e-07</td>\n",
       "      <td>1.201220e-07</td>\n",
       "      <td>3.786964e-09</td>\n",
       "      <td>9.110407e-10</td>\n",
       "      <td>3.386188e-08</td>\n",
       "      <td>2.299786e-08</td>\n",
       "      <td>2.376616e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.252401e-06</td>\n",
       "      <td>1.884281e-07</td>\n",
       "      <td>1.509266e-09</td>\n",
       "      <td>1.732827e-05</td>\n",
       "      <td>1.236111e-08</td>\n",
       "      <td>5.832810e-09</td>\n",
       "      <td>2.373618e-08</td>\n",
       "      <td>2.960161e-06</td>\n",
       "      <td>1.824337e-08</td>\n",
       "      <td>2.186595e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>1503</td>\n",
       "      <td>6.598387e-09</td>\n",
       "      <td>3.567447e-09</td>\n",
       "      <td>9.716860e-09</td>\n",
       "      <td>1.546587e-08</td>\n",
       "      <td>1.087786e-09</td>\n",
       "      <td>3.511639e-08</td>\n",
       "      <td>4.240679e-08</td>\n",
       "      <td>2.755599e-10</td>\n",
       "      <td>3.038170e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.082961e-09</td>\n",
       "      <td>1.469371e-07</td>\n",
       "      <td>1.207967e-11</td>\n",
       "      <td>5.523239e-10</td>\n",
       "      <td>2.306327e-10</td>\n",
       "      <td>1.081219e-12</td>\n",
       "      <td>4.841877e-13</td>\n",
       "      <td>2.041686e-08</td>\n",
       "      <td>1.720291e-07</td>\n",
       "      <td>9.742651e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>1510</td>\n",
       "      <td>5.621800e-07</td>\n",
       "      <td>2.464718e-07</td>\n",
       "      <td>9.404009e-11</td>\n",
       "      <td>8.842223e-11</td>\n",
       "      <td>1.356945e-07</td>\n",
       "      <td>4.498055e-10</td>\n",
       "      <td>1.026827e-07</td>\n",
       "      <td>3.851608e-08</td>\n",
       "      <td>8.879240e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.341001e-06</td>\n",
       "      <td>6.337759e-10</td>\n",
       "      <td>4.817581e-05</td>\n",
       "      <td>5.527043e-08</td>\n",
       "      <td>2.129992e-06</td>\n",
       "      <td>4.094161e-07</td>\n",
       "      <td>8.774744e-05</td>\n",
       "      <td>4.984963e-07</td>\n",
       "      <td>2.331055e-07</td>\n",
       "      <td>2.069650e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>1513</td>\n",
       "      <td>9.339590e-06</td>\n",
       "      <td>5.447415e-07</td>\n",
       "      <td>3.437490e-09</td>\n",
       "      <td>3.866821e-04</td>\n",
       "      <td>4.395955e-07</td>\n",
       "      <td>6.014528e-10</td>\n",
       "      <td>3.097848e-06</td>\n",
       "      <td>6.888536e-07</td>\n",
       "      <td>7.762837e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>8.897467e-07</td>\n",
       "      <td>4.870139e-07</td>\n",
       "      <td>5.019399e-05</td>\n",
       "      <td>6.809178e-07</td>\n",
       "      <td>9.974180e-01</td>\n",
       "      <td>1.077786e-03</td>\n",
       "      <td>2.756926e-06</td>\n",
       "      <td>7.003901e-08</td>\n",
       "      <td>8.212771e-07</td>\n",
       "      <td>5.024040e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>1517</td>\n",
       "      <td>9.235786e-08</td>\n",
       "      <td>2.963776e-08</td>\n",
       "      <td>5.955478e-08</td>\n",
       "      <td>3.372332e-09</td>\n",
       "      <td>5.773778e-08</td>\n",
       "      <td>7.724954e-06</td>\n",
       "      <td>5.286167e-04</td>\n",
       "      <td>1.801382e-09</td>\n",
       "      <td>1.287575e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.579689e-08</td>\n",
       "      <td>1.231442e-07</td>\n",
       "      <td>1.607405e-08</td>\n",
       "      <td>3.275726e-07</td>\n",
       "      <td>1.073762e-06</td>\n",
       "      <td>2.289297e-09</td>\n",
       "      <td>1.329871e-08</td>\n",
       "      <td>7.853971e-10</td>\n",
       "      <td>6.046216e-09</td>\n",
       "      <td>2.108652e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>1522</td>\n",
       "      <td>1.312077e-08</td>\n",
       "      <td>1.217621e-08</td>\n",
       "      <td>3.990652e-08</td>\n",
       "      <td>9.043075e-06</td>\n",
       "      <td>2.275979e-08</td>\n",
       "      <td>5.562875e-09</td>\n",
       "      <td>1.274744e-09</td>\n",
       "      <td>2.097939e-07</td>\n",
       "      <td>3.045319e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.699946e-09</td>\n",
       "      <td>7.009545e-08</td>\n",
       "      <td>9.971583e-08</td>\n",
       "      <td>3.076519e-10</td>\n",
       "      <td>1.340579e-08</td>\n",
       "      <td>1.405745e-05</td>\n",
       "      <td>2.001483e-10</td>\n",
       "      <td>1.781746e-07</td>\n",
       "      <td>1.345210e-09</td>\n",
       "      <td>1.281441e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>1526</td>\n",
       "      <td>5.240042e-08</td>\n",
       "      <td>1.301757e-09</td>\n",
       "      <td>2.654189e-07</td>\n",
       "      <td>9.397294e-06</td>\n",
       "      <td>2.976927e-09</td>\n",
       "      <td>4.306438e-08</td>\n",
       "      <td>8.136275e-07</td>\n",
       "      <td>7.265974e-10</td>\n",
       "      <td>1.249924e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.954807e-08</td>\n",
       "      <td>2.953593e-08</td>\n",
       "      <td>1.775484e-08</td>\n",
       "      <td>2.425441e-06</td>\n",
       "      <td>2.701883e-07</td>\n",
       "      <td>5.655719e-08</td>\n",
       "      <td>8.666208e-11</td>\n",
       "      <td>5.748379e-08</td>\n",
       "      <td>7.055442e-09</td>\n",
       "      <td>2.290165e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>1528</td>\n",
       "      <td>1.083166e-10</td>\n",
       "      <td>1.015971e-07</td>\n",
       "      <td>1.754350e-09</td>\n",
       "      <td>1.830175e-06</td>\n",
       "      <td>1.474213e-07</td>\n",
       "      <td>6.114834e-09</td>\n",
       "      <td>2.602418e-07</td>\n",
       "      <td>8.963455e-10</td>\n",
       "      <td>2.561177e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>5.175333e-11</td>\n",
       "      <td>1.749361e-09</td>\n",
       "      <td>1.736956e-10</td>\n",
       "      <td>4.245520e-10</td>\n",
       "      <td>2.181102e-08</td>\n",
       "      <td>1.923922e-10</td>\n",
       "      <td>6.953832e-11</td>\n",
       "      <td>4.442858e-05</td>\n",
       "      <td>1.227725e-07</td>\n",
       "      <td>1.389856e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>1533</td>\n",
       "      <td>3.274954e-08</td>\n",
       "      <td>4.204460e-07</td>\n",
       "      <td>3.682488e-08</td>\n",
       "      <td>4.636102e-05</td>\n",
       "      <td>2.957090e-07</td>\n",
       "      <td>2.479540e-08</td>\n",
       "      <td>4.224219e-07</td>\n",
       "      <td>2.691234e-07</td>\n",
       "      <td>1.888491e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>4.345647e-09</td>\n",
       "      <td>6.583236e-07</td>\n",
       "      <td>1.073525e-07</td>\n",
       "      <td>2.471443e-08</td>\n",
       "      <td>1.266966e-08</td>\n",
       "      <td>5.297453e-05</td>\n",
       "      <td>5.646772e-09</td>\n",
       "      <td>1.889621e-06</td>\n",
       "      <td>2.212417e-09</td>\n",
       "      <td>4.366302e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1534</td>\n",
       "      <td>3.027612e-06</td>\n",
       "      <td>4.199589e-06</td>\n",
       "      <td>3.659981e-08</td>\n",
       "      <td>1.924179e-06</td>\n",
       "      <td>1.204391e-07</td>\n",
       "      <td>4.062944e-09</td>\n",
       "      <td>4.678412e-08</td>\n",
       "      <td>8.373913e-01</td>\n",
       "      <td>2.118221e-04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.464244e-03</td>\n",
       "      <td>7.009397e-08</td>\n",
       "      <td>1.305962e-06</td>\n",
       "      <td>4.785123e-03</td>\n",
       "      <td>2.037225e-07</td>\n",
       "      <td>3.303371e-05</td>\n",
       "      <td>1.256118e-03</td>\n",
       "      <td>1.203126e-11</td>\n",
       "      <td>5.596458e-10</td>\n",
       "      <td>1.979879e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>1535</td>\n",
       "      <td>1.542627e-08</td>\n",
       "      <td>9.411363e-06</td>\n",
       "      <td>7.209084e-08</td>\n",
       "      <td>5.379039e-06</td>\n",
       "      <td>5.892193e-08</td>\n",
       "      <td>1.157835e-06</td>\n",
       "      <td>2.366368e-05</td>\n",
       "      <td>7.657325e-10</td>\n",
       "      <td>4.688733e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1.996798e-09</td>\n",
       "      <td>1.153121e-06</td>\n",
       "      <td>5.035461e-11</td>\n",
       "      <td>7.279414e-08</td>\n",
       "      <td>1.958485e-07</td>\n",
       "      <td>3.735992e-10</td>\n",
       "      <td>3.493935e-11</td>\n",
       "      <td>1.888148e-04</td>\n",
       "      <td>5.214137e-07</td>\n",
       "      <td>3.967832e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>1537</td>\n",
       "      <td>4.629503e-07</td>\n",
       "      <td>4.727978e-10</td>\n",
       "      <td>1.247968e-12</td>\n",
       "      <td>1.115725e-05</td>\n",
       "      <td>2.075281e-11</td>\n",
       "      <td>1.532668e-13</td>\n",
       "      <td>6.950801e-09</td>\n",
       "      <td>1.599114e-11</td>\n",
       "      <td>2.846372e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>5.414126e-09</td>\n",
       "      <td>4.436131e-10</td>\n",
       "      <td>7.622791e-08</td>\n",
       "      <td>4.503687e-10</td>\n",
       "      <td>9.999424e-01</td>\n",
       "      <td>2.395565e-05</td>\n",
       "      <td>1.694855e-07</td>\n",
       "      <td>2.032533e-09</td>\n",
       "      <td>1.873261e-09</td>\n",
       "      <td>3.948631e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>1540</td>\n",
       "      <td>9.999605e-01</td>\n",
       "      <td>4.476152e-09</td>\n",
       "      <td>4.809666e-08</td>\n",
       "      <td>1.446449e-06</td>\n",
       "      <td>1.165056e-09</td>\n",
       "      <td>8.161998e-09</td>\n",
       "      <td>7.686490e-10</td>\n",
       "      <td>8.024794e-10</td>\n",
       "      <td>4.350565e-06</td>\n",
       "      <td>...</td>\n",
       "      <td>6.115527e-09</td>\n",
       "      <td>1.820703e-08</td>\n",
       "      <td>2.051359e-08</td>\n",
       "      <td>6.053304e-08</td>\n",
       "      <td>4.654711e-07</td>\n",
       "      <td>8.512482e-11</td>\n",
       "      <td>1.859192e-09</td>\n",
       "      <td>1.385988e-10</td>\n",
       "      <td>7.204068e-08</td>\n",
       "      <td>2.948862e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>1542</td>\n",
       "      <td>7.944448e-05</td>\n",
       "      <td>1.862523e-08</td>\n",
       "      <td>5.334211e-10</td>\n",
       "      <td>8.332057e-05</td>\n",
       "      <td>8.769289e-09</td>\n",
       "      <td>8.441043e-11</td>\n",
       "      <td>2.008881e-08</td>\n",
       "      <td>3.999221e-09</td>\n",
       "      <td>2.250154e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>9.467117e-09</td>\n",
       "      <td>2.120110e-08</td>\n",
       "      <td>4.748554e-07</td>\n",
       "      <td>6.667775e-07</td>\n",
       "      <td>1.169947e-03</td>\n",
       "      <td>1.889326e-08</td>\n",
       "      <td>1.176401e-08</td>\n",
       "      <td>1.016336e-08</td>\n",
       "      <td>1.902333e-08</td>\n",
       "      <td>2.035084e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>1546</td>\n",
       "      <td>1.027209e-06</td>\n",
       "      <td>4.901851e-09</td>\n",
       "      <td>1.867717e-07</td>\n",
       "      <td>3.717380e-08</td>\n",
       "      <td>3.966741e-09</td>\n",
       "      <td>5.966528e-09</td>\n",
       "      <td>5.880048e-08</td>\n",
       "      <td>7.136324e-10</td>\n",
       "      <td>9.961934e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>2.649967e-11</td>\n",
       "      <td>1.733624e-10</td>\n",
       "      <td>2.337017e-08</td>\n",
       "      <td>2.062418e-08</td>\n",
       "      <td>3.983649e-09</td>\n",
       "      <td>1.406988e-10</td>\n",
       "      <td>3.378071e-11</td>\n",
       "      <td>5.551406e-07</td>\n",
       "      <td>5.032465e-08</td>\n",
       "      <td>2.719550e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>1553</td>\n",
       "      <td>9.623649e-07</td>\n",
       "      <td>5.031613e-08</td>\n",
       "      <td>2.561091e-06</td>\n",
       "      <td>7.863731e-05</td>\n",
       "      <td>1.384708e-09</td>\n",
       "      <td>9.479377e-10</td>\n",
       "      <td>5.642249e-08</td>\n",
       "      <td>1.063183e-10</td>\n",
       "      <td>2.090407e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>5.622136e-09</td>\n",
       "      <td>1.486724e-06</td>\n",
       "      <td>5.261057e-09</td>\n",
       "      <td>1.734224e-06</td>\n",
       "      <td>8.326165e-06</td>\n",
       "      <td>4.934955e-05</td>\n",
       "      <td>3.346122e-09</td>\n",
       "      <td>2.329431e-08</td>\n",
       "      <td>1.414637e-08</td>\n",
       "      <td>6.024238e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>1558</td>\n",
       "      <td>2.317250e-07</td>\n",
       "      <td>1.196488e-10</td>\n",
       "      <td>6.910878e-14</td>\n",
       "      <td>7.272585e-06</td>\n",
       "      <td>2.369669e-13</td>\n",
       "      <td>1.261685e-13</td>\n",
       "      <td>2.083615e-11</td>\n",
       "      <td>1.006314e-12</td>\n",
       "      <td>1.070816e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>8.440474e-10</td>\n",
       "      <td>1.248342e-13</td>\n",
       "      <td>9.999602e-01</td>\n",
       "      <td>4.853637e-13</td>\n",
       "      <td>1.027847e-06</td>\n",
       "      <td>3.117317e-11</td>\n",
       "      <td>2.950598e-07</td>\n",
       "      <td>3.098461e-11</td>\n",
       "      <td>2.407287e-05</td>\n",
       "      <td>8.625390e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>1560</td>\n",
       "      <td>1.926864e-08</td>\n",
       "      <td>4.583184e-06</td>\n",
       "      <td>4.447730e-05</td>\n",
       "      <td>1.389422e-06</td>\n",
       "      <td>5.047123e-09</td>\n",
       "      <td>5.718789e-06</td>\n",
       "      <td>4.336576e-05</td>\n",
       "      <td>2.905600e-11</td>\n",
       "      <td>3.224893e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>6.196164e-10</td>\n",
       "      <td>1.500053e-06</td>\n",
       "      <td>5.945906e-10</td>\n",
       "      <td>8.786529e-09</td>\n",
       "      <td>5.323207e-08</td>\n",
       "      <td>2.658204e-11</td>\n",
       "      <td>8.779853e-11</td>\n",
       "      <td>1.300371e-05</td>\n",
       "      <td>5.085697e-06</td>\n",
       "      <td>9.752579e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>1564</td>\n",
       "      <td>4.783803e-14</td>\n",
       "      <td>1.567040e-11</td>\n",
       "      <td>6.438892e-11</td>\n",
       "      <td>2.356027e-07</td>\n",
       "      <td>2.107990e-12</td>\n",
       "      <td>1.136206e-13</td>\n",
       "      <td>6.274541e-11</td>\n",
       "      <td>1.382875e-13</td>\n",
       "      <td>2.792403e-13</td>\n",
       "      <td>...</td>\n",
       "      <td>1.101340e-11</td>\n",
       "      <td>4.132727e-08</td>\n",
       "      <td>2.285730e-14</td>\n",
       "      <td>5.058007e-06</td>\n",
       "      <td>1.572344e-09</td>\n",
       "      <td>4.316301e-11</td>\n",
       "      <td>4.043115e-14</td>\n",
       "      <td>1.204316e-06</td>\n",
       "      <td>5.438320e-11</td>\n",
       "      <td>1.153448e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>1565</td>\n",
       "      <td>4.711279e-09</td>\n",
       "      <td>1.498869e-07</td>\n",
       "      <td>8.816682e-09</td>\n",
       "      <td>1.479021e-05</td>\n",
       "      <td>7.929328e-09</td>\n",
       "      <td>2.791848e-08</td>\n",
       "      <td>3.365775e-08</td>\n",
       "      <td>2.284251e-10</td>\n",
       "      <td>8.283986e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>7.131493e-11</td>\n",
       "      <td>7.962306e-09</td>\n",
       "      <td>4.511304e-09</td>\n",
       "      <td>3.414470e-08</td>\n",
       "      <td>1.125772e-07</td>\n",
       "      <td>1.410664e-07</td>\n",
       "      <td>3.277187e-09</td>\n",
       "      <td>1.763028e-04</td>\n",
       "      <td>1.872771e-08</td>\n",
       "      <td>1.188696e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>1567</td>\n",
       "      <td>5.537977e-07</td>\n",
       "      <td>3.401182e-13</td>\n",
       "      <td>4.940448e-13</td>\n",
       "      <td>2.609318e-07</td>\n",
       "      <td>3.391795e-13</td>\n",
       "      <td>2.481562e-13</td>\n",
       "      <td>4.177012e-13</td>\n",
       "      <td>4.796118e-16</td>\n",
       "      <td>4.061341e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>2.095294e-11</td>\n",
       "      <td>7.053536e-10</td>\n",
       "      <td>2.770119e-06</td>\n",
       "      <td>1.107991e-10</td>\n",
       "      <td>2.927417e-06</td>\n",
       "      <td>6.330935e-16</td>\n",
       "      <td>1.256396e-08</td>\n",
       "      <td>1.514438e-10</td>\n",
       "      <td>4.658535e-04</td>\n",
       "      <td>1.156562e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>1573</td>\n",
       "      <td>1.540814e-14</td>\n",
       "      <td>2.008878e-12</td>\n",
       "      <td>1.527573e-10</td>\n",
       "      <td>6.174025e-11</td>\n",
       "      <td>1.443249e-13</td>\n",
       "      <td>2.315802e-12</td>\n",
       "      <td>6.161641e-10</td>\n",
       "      <td>1.797506e-17</td>\n",
       "      <td>9.626731e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>3.462089e-09</td>\n",
       "      <td>2.222173e-10</td>\n",
       "      <td>9.359536e-10</td>\n",
       "      <td>1.016379e-12</td>\n",
       "      <td>1.645184e-08</td>\n",
       "      <td>1.174173e-13</td>\n",
       "      <td>9.175378e-09</td>\n",
       "      <td>2.765117e-07</td>\n",
       "      <td>6.388986e-07</td>\n",
       "      <td>2.914180e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>1576</td>\n",
       "      <td>1.291839e-07</td>\n",
       "      <td>9.999075e-01</td>\n",
       "      <td>6.096479e-08</td>\n",
       "      <td>2.132447e-07</td>\n",
       "      <td>1.393368e-05</td>\n",
       "      <td>2.787074e-09</td>\n",
       "      <td>4.749661e-09</td>\n",
       "      <td>1.560553e-08</td>\n",
       "      <td>1.280619e-05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.818938e-10</td>\n",
       "      <td>6.541649e-09</td>\n",
       "      <td>9.805486e-09</td>\n",
       "      <td>1.170263e-09</td>\n",
       "      <td>5.422747e-11</td>\n",
       "      <td>1.081056e-09</td>\n",
       "      <td>6.967139e-08</td>\n",
       "      <td>2.788114e-10</td>\n",
       "      <td>4.550814e-10</td>\n",
       "      <td>4.035324e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>1577</td>\n",
       "      <td>1.202606e-07</td>\n",
       "      <td>8.535591e-07</td>\n",
       "      <td>1.248223e-09</td>\n",
       "      <td>1.604104e-05</td>\n",
       "      <td>4.773889e-09</td>\n",
       "      <td>9.082250e-11</td>\n",
       "      <td>2.854560e-06</td>\n",
       "      <td>1.611732e-07</td>\n",
       "      <td>3.610118e-04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.577478e-08</td>\n",
       "      <td>4.851389e-09</td>\n",
       "      <td>5.101046e-05</td>\n",
       "      <td>1.908416e-10</td>\n",
       "      <td>9.686188e-03</td>\n",
       "      <td>6.595326e-03</td>\n",
       "      <td>5.815748e-06</td>\n",
       "      <td>6.340596e-09</td>\n",
       "      <td>4.762732e-08</td>\n",
       "      <td>1.354127e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>1579</td>\n",
       "      <td>3.906011e-07</td>\n",
       "      <td>4.498634e-09</td>\n",
       "      <td>5.890326e-08</td>\n",
       "      <td>9.094561e-10</td>\n",
       "      <td>1.773349e-08</td>\n",
       "      <td>3.504384e-08</td>\n",
       "      <td>2.001469e-09</td>\n",
       "      <td>9.098496e-11</td>\n",
       "      <td>3.041764e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>6.929402e-12</td>\n",
       "      <td>1.218037e-11</td>\n",
       "      <td>9.068458e-10</td>\n",
       "      <td>1.474777e-09</td>\n",
       "      <td>2.788112e-10</td>\n",
       "      <td>8.639944e-13</td>\n",
       "      <td>9.452204e-12</td>\n",
       "      <td>3.678355e-07</td>\n",
       "      <td>4.050781e-09</td>\n",
       "      <td>1.777693e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>1580</td>\n",
       "      <td>3.411929e-10</td>\n",
       "      <td>4.321245e-09</td>\n",
       "      <td>6.431056e-08</td>\n",
       "      <td>9.612141e-07</td>\n",
       "      <td>3.032472e-08</td>\n",
       "      <td>6.231275e-09</td>\n",
       "      <td>9.274294e-08</td>\n",
       "      <td>6.284370e-06</td>\n",
       "      <td>2.288603e-11</td>\n",
       "      <td>...</td>\n",
       "      <td>2.555226e-07</td>\n",
       "      <td>4.175479e-06</td>\n",
       "      <td>3.118484e-13</td>\n",
       "      <td>2.321441e-04</td>\n",
       "      <td>3.755473e-09</td>\n",
       "      <td>1.115125e-07</td>\n",
       "      <td>9.307360e-12</td>\n",
       "      <td>2.196070e-09</td>\n",
       "      <td>3.290000e-10</td>\n",
       "      <td>1.782840e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>1583</td>\n",
       "      <td>1.931912e-14</td>\n",
       "      <td>2.407064e-08</td>\n",
       "      <td>1.293203e-10</td>\n",
       "      <td>2.762237e-07</td>\n",
       "      <td>2.021890e-11</td>\n",
       "      <td>1.517825e-07</td>\n",
       "      <td>1.026834e-06</td>\n",
       "      <td>2.481629e-13</td>\n",
       "      <td>1.776014e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>2.722312e-12</td>\n",
       "      <td>6.912236e-13</td>\n",
       "      <td>2.499756e-14</td>\n",
       "      <td>2.644051e-12</td>\n",
       "      <td>1.830081e-11</td>\n",
       "      <td>1.346635e-08</td>\n",
       "      <td>1.203467e-12</td>\n",
       "      <td>8.135627e-10</td>\n",
       "      <td>7.362491e-12</td>\n",
       "      <td>3.685807e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>594 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  Acer_Capillipes  Acer_Circinatum     Acer_Mono   Acer_Opalus  \\\n",
       "0       4     5.564099e-09     3.995893e-09  3.215826e-10  3.579184e-05   \n",
       "1       7     4.234810e-09     3.445131e-08  1.008688e-07  1.249469e-06   \n",
       "2       9     9.157161e-08     9.980009e-01  1.720776e-07  3.295709e-07   \n",
       "3      12     3.137234e-10     7.157261e-05  4.558301e-08  2.259347e-09   \n",
       "4      13     5.795870e-08     1.015203e-07  1.287603e-10  9.806435e-10   \n",
       "5      16     2.953344e-06     6.158201e-07  9.883947e-07  9.215780e-01   \n",
       "6      19     2.618619e-06     1.213384e-06  1.164345e-06  9.973845e-01   \n",
       "7      23     1.599135e-10     5.748869e-06  1.504531e-06  2.391018e-06   \n",
       "8      24     9.315323e-07     1.018916e-07  1.891389e-08  9.358384e-11   \n",
       "9      28     1.210373e-04     2.806766e-05  1.315054e-07  8.721123e-08   \n",
       "10     33     3.932919e-08     2.756033e-09  5.856367e-06  3.652184e-06   \n",
       "11     36     1.406331e-05     3.388038e-09  2.383083e-08  7.584937e-10   \n",
       "12     39     1.572562e-06     1.396278e-11  1.645762e-07  2.428697e-07   \n",
       "13     41     9.944187e-08     5.705782e-09  5.300804e-05  7.948324e-07   \n",
       "14     44     2.618808e-11     8.096802e-11  1.504561e-11  1.771722e-06   \n",
       "15     46     1.986400e-08     6.700429e-07  6.823167e-07  1.535088e-04   \n",
       "16     47     1.783843e-07     2.013781e-07  5.944322e-05  4.065487e-09   \n",
       "17     51     1.308695e-07     1.292476e-07  3.761577e-04  1.588583e-07   \n",
       "18     52     3.626835e-07     1.208955e-06  3.527771e-08  4.542050e-08   \n",
       "19     53     1.544654e-06     1.428886e-08  1.318026e-10  4.026909e-09   \n",
       "20     57     1.678146e-10     4.611596e-12  2.955799e-08  6.005782e-12   \n",
       "21     59     8.913486e-06     5.710220e-10  2.428150e-04  2.099650e-07   \n",
       "22     62     4.317981e-14     5.068797e-12  2.504857e-12  2.717345e-10   \n",
       "23     65     3.790941e-14     6.894957e-12  4.189668e-08  1.024114e-11   \n",
       "24     68     3.667399e-09     9.996654e-01  6.208755e-09  2.260363e-09   \n",
       "25     70     7.649607e-09     1.415651e-08  1.817908e-06  1.579273e-08   \n",
       "26     74     2.309302e-12     2.145764e-08  3.529617e-08  1.784948e-07   \n",
       "27     77     9.740345e-17     1.926140e-10  1.063341e-12  6.884135e-09   \n",
       "28     79     7.727279e-06     2.836519e-08  3.864822e-07  4.951246e-09   \n",
       "29     86     1.078814e-09     4.471940e-10  1.269325e-07  9.037996e-10   \n",
       "..    ...              ...              ...           ...           ...   \n",
       "564  1493     1.441340e-07     5.486910e-05  1.550863e-05  8.767483e-08   \n",
       "565  1495     4.566245e-10     8.251230e-10  9.165857e-07  8.792121e-10   \n",
       "566  1497     3.661469e-04     1.309526e-03  1.190103e-08  3.419827e-06   \n",
       "567  1498     2.372804e-11     4.854597e-09  8.507824e-07  1.201220e-07   \n",
       "568  1503     6.598387e-09     3.567447e-09  9.716860e-09  1.546587e-08   \n",
       "569  1510     5.621800e-07     2.464718e-07  9.404009e-11  8.842223e-11   \n",
       "570  1513     9.339590e-06     5.447415e-07  3.437490e-09  3.866821e-04   \n",
       "571  1517     9.235786e-08     2.963776e-08  5.955478e-08  3.372332e-09   \n",
       "572  1522     1.312077e-08     1.217621e-08  3.990652e-08  9.043075e-06   \n",
       "573  1526     5.240042e-08     1.301757e-09  2.654189e-07  9.397294e-06   \n",
       "574  1528     1.083166e-10     1.015971e-07  1.754350e-09  1.830175e-06   \n",
       "575  1533     3.274954e-08     4.204460e-07  3.682488e-08  4.636102e-05   \n",
       "576  1534     3.027612e-06     4.199589e-06  3.659981e-08  1.924179e-06   \n",
       "577  1535     1.542627e-08     9.411363e-06  7.209084e-08  5.379039e-06   \n",
       "578  1537     4.629503e-07     4.727978e-10  1.247968e-12  1.115725e-05   \n",
       "579  1540     9.999605e-01     4.476152e-09  4.809666e-08  1.446449e-06   \n",
       "580  1542     7.944448e-05     1.862523e-08  5.334211e-10  8.332057e-05   \n",
       "581  1546     1.027209e-06     4.901851e-09  1.867717e-07  3.717380e-08   \n",
       "582  1553     9.623649e-07     5.031613e-08  2.561091e-06  7.863731e-05   \n",
       "583  1558     2.317250e-07     1.196488e-10  6.910878e-14  7.272585e-06   \n",
       "584  1560     1.926864e-08     4.583184e-06  4.447730e-05  1.389422e-06   \n",
       "585  1564     4.783803e-14     1.567040e-11  6.438892e-11  2.356027e-07   \n",
       "586  1565     4.711279e-09     1.498869e-07  8.816682e-09  1.479021e-05   \n",
       "587  1567     5.537977e-07     3.401182e-13  4.940448e-13  2.609318e-07   \n",
       "588  1573     1.540814e-14     2.008878e-12  1.527573e-10  6.174025e-11   \n",
       "589  1576     1.291839e-07     9.999075e-01  6.096479e-08  2.132447e-07   \n",
       "590  1577     1.202606e-07     8.535591e-07  1.248223e-09  1.604104e-05   \n",
       "591  1579     3.906011e-07     4.498634e-09  5.890326e-08  9.094561e-10   \n",
       "592  1580     3.411929e-10     4.321245e-09  6.431056e-08  9.612141e-07   \n",
       "593  1583     1.931912e-14     2.407064e-08  1.293203e-10  2.762237e-07   \n",
       "\n",
       "     Acer_Palmatum   Acer_Pictum  Acer_Platanoids   Acer_Rubrum  \\\n",
       "0     1.534015e-09  3.922370e-11     3.727405e-08  2.401681e-09   \n",
       "1     1.099376e-07  3.611913e-08     2.375303e-06  1.860793e-08   \n",
       "2     1.197206e-03  1.378777e-06     2.659681e-08  3.054111e-05   \n",
       "3     2.805615e-07  1.204050e-09     2.721501e-05  4.671056e-08   \n",
       "4     3.148348e-08  5.977766e-11     3.276833e-07  8.885695e-09   \n",
       "5     1.191772e-06  1.115861e-07     1.191132e-05  2.849477e-04   \n",
       "6     1.977085e-07  3.813481e-08     1.879613e-06  5.791098e-07   \n",
       "7     1.457821e-07  9.795280e-05     2.314170e-06  8.684391e-10   \n",
       "8     5.781123e-08  1.202895e-06     1.173221e-06  2.271810e-08   \n",
       "9     2.161358e-07  3.098198e-08     2.940067e-06  3.875687e-05   \n",
       "10    1.089351e-07  1.034257e-06     1.277500e-07  1.355123e-09   \n",
       "11    1.929676e-07  1.492650e-08     1.542286e-09  4.010165e-08   \n",
       "12    4.341697e-10  7.826118e-09     1.135842e-10  1.228455e-09   \n",
       "13    5.958057e-09  2.822317e-08     5.388822e-08  2.926009e-08   \n",
       "14    4.521733e-09  2.988362e-10     7.441791e-10  1.249217e-08   \n",
       "15    2.241645e-08  2.329560e-08     7.488351e-06  1.269853e-09   \n",
       "16    5.624620e-08  6.533107e-07     1.663459e-04  2.999145e-08   \n",
       "17    3.841262e-07  5.976147e-06     4.282566e-07  6.127383e-05   \n",
       "18    5.561457e-07  3.110689e-08     2.573891e-06  6.797789e-04   \n",
       "19    4.878647e-07  1.790830e-05     3.388427e-07  9.453548e-10   \n",
       "20    7.919481e-10  1.440238e-10     2.978474e-13  1.056984e-12   \n",
       "21    9.655992e-08  4.709331e-08     5.804229e-09  9.371228e-09   \n",
       "22    9.557354e-11  7.302641e-10     4.624729e-11  9.271926e-12   \n",
       "23    2.518811e-11  2.123407e-11     3.491687e-06  3.217698e-14   \n",
       "24    1.224216e-06  2.777808e-09     3.994571e-08  2.146493e-07   \n",
       "25    8.333610e-08  3.061456e-07     2.402332e-09  1.936109e-09   \n",
       "26    8.645630e-08  9.718477e-06     4.785320e-08  4.965027e-10   \n",
       "27    6.317468e-12  4.087843e-09     1.776700e-07  1.853469e-13   \n",
       "28    4.390390e-07  2.406830e-05     4.178570e-09  3.992743e-10   \n",
       "29    7.564464e-09  7.925620e-08     5.192035e-10  5.829949e-11   \n",
       "..             ...           ...              ...           ...   \n",
       "564   3.547741e-05  5.675403e-05     1.721817e-05  1.176765e-07   \n",
       "565   1.188359e-08  1.452854e-08     5.592016e-09  7.139153e-07   \n",
       "566   4.901422e-05  4.108013e-08     1.337763e-07  1.152306e-05   \n",
       "567   3.786964e-09  9.110407e-10     3.386188e-08  2.299786e-08   \n",
       "568   1.087786e-09  3.511639e-08     4.240679e-08  2.755599e-10   \n",
       "569   1.356945e-07  4.498055e-10     1.026827e-07  3.851608e-08   \n",
       "570   4.395955e-07  6.014528e-10     3.097848e-06  6.888536e-07   \n",
       "571   5.773778e-08  7.724954e-06     5.286167e-04  1.801382e-09   \n",
       "572   2.275979e-08  5.562875e-09     1.274744e-09  2.097939e-07   \n",
       "573   2.976927e-09  4.306438e-08     8.136275e-07  7.265974e-10   \n",
       "574   1.474213e-07  6.114834e-09     2.602418e-07  8.963455e-10   \n",
       "575   2.957090e-07  2.479540e-08     4.224219e-07  2.691234e-07   \n",
       "576   1.204391e-07  4.062944e-09     4.678412e-08  8.373913e-01   \n",
       "577   5.892193e-08  1.157835e-06     2.366368e-05  7.657325e-10   \n",
       "578   2.075281e-11  1.532668e-13     6.950801e-09  1.599114e-11   \n",
       "579   1.165056e-09  8.161998e-09     7.686490e-10  8.024794e-10   \n",
       "580   8.769289e-09  8.441043e-11     2.008881e-08  3.999221e-09   \n",
       "581   3.966741e-09  5.966528e-09     5.880048e-08  7.136324e-10   \n",
       "582   1.384708e-09  9.479377e-10     5.642249e-08  1.063183e-10   \n",
       "583   2.369669e-13  1.261685e-13     2.083615e-11  1.006314e-12   \n",
       "584   5.047123e-09  5.718789e-06     4.336576e-05  2.905600e-11   \n",
       "585   2.107990e-12  1.136206e-13     6.274541e-11  1.382875e-13   \n",
       "586   7.929328e-09  2.791848e-08     3.365775e-08  2.284251e-10   \n",
       "587   3.391795e-13  2.481562e-13     4.177012e-13  4.796118e-16   \n",
       "588   1.443249e-13  2.315802e-12     6.161641e-10  1.797506e-17   \n",
       "589   1.393368e-05  2.787074e-09     4.749661e-09  1.560553e-08   \n",
       "590   4.773889e-09  9.082250e-11     2.854560e-06  1.611732e-07   \n",
       "591   1.773349e-08  3.504384e-08     2.001469e-09  9.098496e-11   \n",
       "592   3.032472e-08  6.231275e-09     9.274294e-08  6.284370e-06   \n",
       "593   2.021890e-11  1.517825e-07     1.026834e-06  2.481629e-13   \n",
       "\n",
       "     Acer_Rufinerve       ...         Salix_Fragilis  Salix_Intergra  \\\n",
       "0      4.123072e-10       ...           6.463605e-10    5.186318e-06   \n",
       "1      5.416962e-09       ...           2.938037e-09    3.328846e-08   \n",
       "2      2.208277e-05       ...           3.714409e-07    1.045037e-07   \n",
       "3      7.129248e-05       ...           1.857425e-07    2.992218e-09   \n",
       "4      2.571370e-06       ...           5.008176e-07    1.483564e-09   \n",
       "5      2.764408e-06       ...           2.062067e-04    2.890773e-05   \n",
       "6      9.794209e-08       ...           1.361041e-07    5.301088e-06   \n",
       "7      7.397863e-11       ...           4.542820e-09    1.274733e-07   \n",
       "8      7.574131e-06       ...           8.600087e-09    1.222477e-09   \n",
       "9      9.994276e-01       ...           7.775509e-06    2.366486e-07   \n",
       "10     1.215952e-11       ...           5.391952e-07    1.112631e-07   \n",
       "11     3.703175e-07       ...           1.573415e-10    1.088290e-10   \n",
       "12     1.608048e-10       ...           4.938538e-08    2.732019e-06   \n",
       "13     3.137843e-09       ...           2.338334e-07    1.676785e-07   \n",
       "14     4.900608e-08       ...           3.347493e-08    1.993373e-09   \n",
       "15     1.521007e-09       ...           1.012804e-08    5.993997e-09   \n",
       "16     8.789836e-06       ...           7.026609e-09    9.431191e-07   \n",
       "17     6.198897e-10       ...           5.834585e-07    2.037118e-05   \n",
       "18     4.081283e-06       ...           1.629303e-05    1.626204e-08   \n",
       "19     8.161784e-11       ...           3.031005e-08    3.323287e-06   \n",
       "20     3.973030e-14       ...           4.533909e-10    1.249250e-06   \n",
       "21     6.285596e-09       ...           1.369631e-08    8.120645e-06   \n",
       "22     9.487337e-15       ...           6.298651e-08    1.481508e-12   \n",
       "23     6.335027e-09       ...           1.069225e-09    6.288038e-10   \n",
       "24     2.636226e-07       ...           3.232613e-08    6.490126e-08   \n",
       "25     1.669763e-09       ...           2.172881e-09    1.132690e-07   \n",
       "26     1.570151e-10       ...           1.350725e-07    3.096884e-09   \n",
       "27     1.168438e-12       ...           1.070038e-11    1.733703e-13   \n",
       "28     4.132396e-08       ...           1.896494e-08    1.535780e-07   \n",
       "29     1.029941e-09       ...           6.981262e-11    5.602633e-10   \n",
       "..              ...       ...                    ...             ...   \n",
       "564    2.184806e-08       ...           2.107479e-05    3.124743e-07   \n",
       "565    5.120421e-11       ...           7.506396e-07    1.060539e-07   \n",
       "566    1.507953e-03       ...           3.208211e-05    1.657194e-07   \n",
       "567    2.376616e-09       ...           1.252401e-06    1.884281e-07   \n",
       "568    3.038170e-09       ...           3.082961e-09    1.469371e-07   \n",
       "569    8.879240e-06       ...           2.341001e-06    6.337759e-10   \n",
       "570    7.762837e-06       ...           8.897467e-07    4.870139e-07   \n",
       "571    1.287575e-07       ...           1.579689e-08    1.231442e-07   \n",
       "572    3.045319e-07       ...           1.699946e-09    7.009545e-08   \n",
       "573    1.249924e-09       ...           1.954807e-08    2.953593e-08   \n",
       "574    2.561177e-09       ...           5.175333e-11    1.749361e-09   \n",
       "575    1.888491e-08       ...           4.345647e-09    6.583236e-07   \n",
       "576    2.118221e-04       ...           1.464244e-03    7.009397e-08   \n",
       "577    4.688733e-08       ...           1.996798e-09    1.153121e-06   \n",
       "578    2.846372e-07       ...           5.414126e-09    4.436131e-10   \n",
       "579    4.350565e-06       ...           6.115527e-09    1.820703e-08   \n",
       "580    2.250154e-07       ...           9.467117e-09    2.120110e-08   \n",
       "581    9.961934e-08       ...           2.649967e-11    1.733624e-10   \n",
       "582    2.090407e-08       ...           5.622136e-09    1.486724e-06   \n",
       "583    1.070816e-10       ...           8.440474e-10    1.248342e-13   \n",
       "584    3.224893e-07       ...           6.196164e-10    1.500053e-06   \n",
       "585    2.792403e-13       ...           1.101340e-11    4.132727e-08   \n",
       "586    8.283986e-10       ...           7.131493e-11    7.962306e-09   \n",
       "587    4.061341e-15       ...           2.095294e-11    7.053536e-10   \n",
       "588    9.626731e-15       ...           3.462089e-09    2.222173e-10   \n",
       "589    1.280619e-05       ...           1.818938e-10    6.541649e-09   \n",
       "590    3.610118e-04       ...           1.577478e-08    4.851389e-09   \n",
       "591    3.041764e-09       ...           6.929402e-12    1.218037e-11   \n",
       "592    2.288603e-11       ...           2.555226e-07    4.175479e-06   \n",
       "593    1.776014e-10       ...           2.722312e-12    6.912236e-13   \n",
       "\n",
       "      Sorbus_Aria  Tilia_Oliveri  Tilia_Platyphyllos  Tilia_Tomentosa  \\\n",
       "0    1.572735e-08   2.441709e-09        4.433265e-07     1.181075e-09   \n",
       "1    9.049756e-09   1.432459e-08        4.233897e-09     2.621590e-06   \n",
       "2    1.498809e-07   2.413588e-08        1.329892e-10     8.576847e-08   \n",
       "3    2.208566e-06   3.269031e-08        2.777161e-07     4.933698e-06   \n",
       "4    8.166779e-06   4.322440e-08        1.759922e-05     3.189810e-06   \n",
       "5    2.903316e-05   6.455876e-05        2.516816e-04     2.165608e-04   \n",
       "6    4.502169e-06   1.781421e-06        2.038559e-05     1.202596e-04   \n",
       "7    3.446437e-09   2.159990e-09        4.277613e-07     1.438084e-09   \n",
       "8    4.911558e-10   1.202762e-07        5.178366e-10     7.382057e-10   \n",
       "9    1.401758e-05   1.583462e-07        1.801642e-06     1.189907e-04   \n",
       "10   4.638495e-07   2.243190e-05        1.238102e-07     2.200176e-11   \n",
       "11   5.676565e-07   4.219507e-09        4.645312e-08     9.208544e-11   \n",
       "12   4.861549e-11   6.666294e-06        5.054454e-09     3.875538e-10   \n",
       "13   6.979811e-09   1.269386e-05        3.549302e-08     1.344781e-04   \n",
       "14   2.955677e-10   5.435498e-08        4.676059e-08     1.454506e-08   \n",
       "15   3.414696e-04   2.881139e-06        7.856298e-06     1.338125e-06   \n",
       "16   6.480544e-08   3.523070e-07        2.817687e-09     1.409496e-09   \n",
       "17   4.348942e-10   7.464701e-06        1.843490e-09     1.038107e-06   \n",
       "18   6.951186e-07   1.856436e-04        1.960887e-07     1.284283e-05   \n",
       "19   2.597561e-11   1.914445e-07        2.502503e-08     1.631993e-10   \n",
       "20   9.606800e-14   2.562269e-12        2.523007e-08     5.376545e-14   \n",
       "21   7.053135e-09   1.008887e-06        8.984427e-07     5.350731e-08   \n",
       "22   2.760454e-13   2.509549e-11        2.007160e-13     3.998215e-13   \n",
       "23   3.415210e-09   2.757435e-10        9.946258e-07     7.224162e-11   \n",
       "24   1.237012e-08   1.898051e-09        3.874600e-10     5.196485e-08   \n",
       "25   3.614806e-08   6.683633e-09        2.806353e-07     1.614547e-10   \n",
       "26   6.011512e-10   1.921669e-07        3.913812e-09     1.665448e-09   \n",
       "27   2.093995e-13   6.106769e-13        2.351385e-13     1.308822e-10   \n",
       "28   7.601535e-09   2.080706e-07        1.015040e-09     3.403680e-12   \n",
       "29   1.499131e-09   4.790459e-10        6.378123e-09     1.651472e-11   \n",
       "..            ...            ...                 ...              ...   \n",
       "564  9.709215e-05   1.624309e-04        8.408316e-07     8.668955e-09   \n",
       "565  1.874987e-11   9.985044e-01        3.573243e-09     6.257854e-09   \n",
       "566  5.789270e-05   1.669847e-06        7.731309e-04     8.956444e-05   \n",
       "567  1.509266e-09   1.732827e-05        1.236111e-08     5.832810e-09   \n",
       "568  1.207967e-11   5.523239e-10        2.306327e-10     1.081219e-12   \n",
       "569  4.817581e-05   5.527043e-08        2.129992e-06     4.094161e-07   \n",
       "570  5.019399e-05   6.809178e-07        9.974180e-01     1.077786e-03   \n",
       "571  1.607405e-08   3.275726e-07        1.073762e-06     2.289297e-09   \n",
       "572  9.971583e-08   3.076519e-10        1.340579e-08     1.405745e-05   \n",
       "573  1.775484e-08   2.425441e-06        2.701883e-07     5.655719e-08   \n",
       "574  1.736956e-10   4.245520e-10        2.181102e-08     1.923922e-10   \n",
       "575  1.073525e-07   2.471443e-08        1.266966e-08     5.297453e-05   \n",
       "576  1.305962e-06   4.785123e-03        2.037225e-07     3.303371e-05   \n",
       "577  5.035461e-11   7.279414e-08        1.958485e-07     3.735992e-10   \n",
       "578  7.622791e-08   4.503687e-10        9.999424e-01     2.395565e-05   \n",
       "579  2.051359e-08   6.053304e-08        4.654711e-07     8.512482e-11   \n",
       "580  4.748554e-07   6.667775e-07        1.169947e-03     1.889326e-08   \n",
       "581  2.337017e-08   2.062418e-08        3.983649e-09     1.406988e-10   \n",
       "582  5.261057e-09   1.734224e-06        8.326165e-06     4.934955e-05   \n",
       "583  9.999602e-01   4.853637e-13        1.027847e-06     3.117317e-11   \n",
       "584  5.945906e-10   8.786529e-09        5.323207e-08     2.658204e-11   \n",
       "585  2.285730e-14   5.058007e-06        1.572344e-09     4.316301e-11   \n",
       "586  4.511304e-09   3.414470e-08        1.125772e-07     1.410664e-07   \n",
       "587  2.770119e-06   1.107991e-10        2.927417e-06     6.330935e-16   \n",
       "588  9.359536e-10   1.016379e-12        1.645184e-08     1.174173e-13   \n",
       "589  9.805486e-09   1.170263e-09        5.422747e-11     1.081056e-09   \n",
       "590  5.101046e-05   1.908416e-10        9.686188e-03     6.595326e-03   \n",
       "591  9.068458e-10   1.474777e-09        2.788112e-10     8.639944e-13   \n",
       "592  3.118484e-13   2.321441e-04        3.755473e-09     1.115125e-07   \n",
       "593  2.499756e-14   2.644051e-12        1.830081e-11     1.346635e-08   \n",
       "\n",
       "     Ulmus_Bergmanniana  Viburnum_Tinus  Viburnum_x_Rhytidophylloides  \\\n",
       "0          2.073514e-11    3.742596e-10                  1.466789e-09   \n",
       "1          1.648111e-09    1.533108e-06                  2.634925e-09   \n",
       "2          2.674542e-07    1.345941e-09                  4.951499e-09   \n",
       "3          2.635645e-03    1.132239e-07                  6.438043e-07   \n",
       "4          7.981434e-05    2.553334e-07                  3.665428e-08   \n",
       "5          2.039107e-06    1.924955e-06                  2.673305e-04   \n",
       "6          2.928770e-08    2.013095e-06                  8.148651e-06   \n",
       "7          2.114243e-10    2.195948e-07                  5.251225e-08   \n",
       "8          5.323598e-11    4.985075e-12                  2.322560e-11   \n",
       "9          3.301221e-06    4.633716e-11                  4.395520e-09   \n",
       "10         1.763415e-06    6.877352e-04                  1.250015e-03   \n",
       "11         1.955501e-09    7.486872e-09                  3.623627e-09   \n",
       "12         7.182437e-11    4.052775e-07                  5.120274e-08   \n",
       "13         2.228624e-09    3.995197e-11                  2.528558e-08   \n",
       "14         1.280033e-11    9.011586e-06                  1.294304e-11   \n",
       "15         1.577276e-05    5.722030e-06                  3.518631e-06   \n",
       "16         5.072697e-11    3.400663e-11                  7.061470e-10   \n",
       "17         2.044559e-09    1.201631e-09                  4.101938e-07   \n",
       "18         2.860258e-05    1.947592e-08                  1.219842e-08   \n",
       "19         1.975647e-09    1.277459e-11                  1.096317e-10   \n",
       "20         1.050258e-12    4.435981e-05                  3.083702e-09   \n",
       "21         6.599065e-10    7.458794e-05                  1.273415e-05   \n",
       "22         2.141353e-12    2.007918e-10                  7.583069e-13   \n",
       "23         1.365386e-06    3.676520e-05                  7.906191e-05   \n",
       "24         5.991071e-07    2.160162e-10                  2.052270e-09   \n",
       "25         2.447734e-08    1.755850e-05                  5.479419e-07   \n",
       "26         2.717474e-09    6.935725e-06                  4.779661e-09   \n",
       "27         5.477730e-12    8.137219e-12                  1.805210e-12   \n",
       "28         1.073434e-09    3.315712e-13                  6.934154e-07   \n",
       "29         3.438645e-10    8.933652e-07                  9.562712e-08   \n",
       "..                  ...             ...                           ...   \n",
       "564        5.247108e-04    5.594857e-05                  3.266238e-05   \n",
       "565        9.867460e-11    9.440727e-09                  1.119859e-09   \n",
       "566        4.984578e-04    9.657998e-08                  1.158567e-07   \n",
       "567        2.373618e-08    2.960161e-06                  1.824337e-08   \n",
       "568        4.841877e-13    2.041686e-08                  1.720291e-07   \n",
       "569        8.774744e-05    4.984963e-07                  2.331055e-07   \n",
       "570        2.756926e-06    7.003901e-08                  8.212771e-07   \n",
       "571        1.329871e-08    7.853971e-10                  6.046216e-09   \n",
       "572        2.001483e-10    1.781746e-07                  1.345210e-09   \n",
       "573        8.666208e-11    5.748379e-08                  7.055442e-09   \n",
       "574        6.953832e-11    4.442858e-05                  1.227725e-07   \n",
       "575        5.646772e-09    1.889621e-06                  2.212417e-09   \n",
       "576        1.256118e-03    1.203126e-11                  5.596458e-10   \n",
       "577        3.493935e-11    1.888148e-04                  5.214137e-07   \n",
       "578        1.694855e-07    2.032533e-09                  1.873261e-09   \n",
       "579        1.859192e-09    1.385988e-10                  7.204068e-08   \n",
       "580        1.176401e-08    1.016336e-08                  1.902333e-08   \n",
       "581        3.378071e-11    5.551406e-07                  5.032465e-08   \n",
       "582        3.346122e-09    2.329431e-08                  1.414637e-08   \n",
       "583        2.950598e-07    3.098461e-11                  2.407287e-05   \n",
       "584        8.779853e-11    1.300371e-05                  5.085697e-06   \n",
       "585        4.043115e-14    1.204316e-06                  5.438320e-11   \n",
       "586        3.277187e-09    1.763028e-04                  1.872771e-08   \n",
       "587        1.256396e-08    1.514438e-10                  4.658535e-04   \n",
       "588        9.175378e-09    2.765117e-07                  6.388986e-07   \n",
       "589        6.967139e-08    2.788114e-10                  4.550814e-10   \n",
       "590        5.815748e-06    6.340596e-09                  4.762732e-08   \n",
       "591        9.452204e-12    3.678355e-07                  4.050781e-09   \n",
       "592        9.307360e-12    2.196070e-09                  3.290000e-10   \n",
       "593        1.203467e-12    8.135627e-10                  7.362491e-12   \n",
       "\n",
       "     Zelkova_Serrata  \n",
       "0       6.290616e-10  \n",
       "1       6.199036e-06  \n",
       "2       2.277303e-04  \n",
       "3       3.766651e-04  \n",
       "4       1.496972e-09  \n",
       "5       1.474009e-05  \n",
       "6       4.414580e-04  \n",
       "7       1.933370e-07  \n",
       "8       6.856449e-11  \n",
       "9       1.093372e-06  \n",
       "10      1.819961e-06  \n",
       "11      2.566271e-08  \n",
       "12      2.668129e-08  \n",
       "13      1.299757e-07  \n",
       "14      1.260969e-07  \n",
       "15      8.212031e-06  \n",
       "16      1.770474e-10  \n",
       "17      1.466581e-07  \n",
       "18      1.370336e-06  \n",
       "19      3.585779e-11  \n",
       "20      2.289574e-11  \n",
       "21      4.774234e-05  \n",
       "22      2.055387e-12  \n",
       "23      1.038370e-10  \n",
       "24      7.264055e-05  \n",
       "25      8.682599e-10  \n",
       "26      1.495983e-08  \n",
       "27      1.451984e-14  \n",
       "28      3.267973e-11  \n",
       "29      1.653262e-11  \n",
       "..               ...  \n",
       "564     6.672587e-07  \n",
       "565     1.984872e-09  \n",
       "566     2.675364e-06  \n",
       "567     2.186595e-07  \n",
       "568     9.742651e-10  \n",
       "569     2.069650e-09  \n",
       "570     5.024040e-07  \n",
       "571     2.108652e-11  \n",
       "572     1.281441e-05  \n",
       "573     2.290165e-09  \n",
       "574     1.389856e-06  \n",
       "575     4.366302e-05  \n",
       "576     1.979879e-07  \n",
       "577     3.967832e-08  \n",
       "578     3.948631e-11  \n",
       "579     2.948862e-08  \n",
       "580     2.035084e-10  \n",
       "581     2.719550e-07  \n",
       "582     6.024238e-07  \n",
       "583     8.625390e-09  \n",
       "584     9.752579e-07  \n",
       "585     1.153448e-08  \n",
       "586     1.188696e-04  \n",
       "587     1.156562e-11  \n",
       "588     2.914180e-10  \n",
       "589     4.035324e-05  \n",
       "590     1.354127e-04  \n",
       "591     1.777693e-09  \n",
       "592     1.782840e-09  \n",
       "593     3.685807e-10  \n",
       "\n",
       "[594 rows x 100 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_df = pd.DataFrame(preds_test, columns=data.le.classes_)\n",
    "ids_test_df = pd.DataFrame(ids_test, columns=[\"id\"])\n",
    "submission = pd.concat([ids_test_df, preds_df], axis=1)\n",
    "submission.to_csv('submission_mlp.csv', index=False)\n",
    "# below prints the submission, can be removed and replaced with code block below\n",
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upload submission\n",
    "\n",
    "1. Go to `https://www.kaggle.com/c/leaf-classification/`\n",
    "2. Make a submission\n",
    "3. Click or drop your submission here (writing a description is good practice)\n",
    "4. Submit\n",
    "\n",
    "Success! now you can view your score on the leaderboard, try and see if you can beat me! (Alexander Rosenberg Johansen: 0.06399)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "\n",
    "When doing these exercises nothing is sacred, you can change learning rate, try testing various learning rates, batch sizes, validation sizes, etc. And most importantly, the validation set is very small (only 1 sample per class), so try different seeds if evaluating the same model twice.\n",
    "\n",
    "Describe how each of below tasks effects training:\n",
    "\n",
    "1. Include l2 (fully connected and dropout layer)\n",
    "2. Set DROPOUT to TRUE\n",
    "3. Include L2 regularization\n",
    "4. Try with L1 regularization (look at [tensorflow API](https://www.tensorflow.org/versions/r0.10/api_docs/python/contrib.layers.html#regularizers) for instructions)\n",
    "5. Use only the image for training (with CNN) - comment on the increased time between iterations.\n",
    "6. Use dropout between the convolutional layers\n",
    "7. Include the RNN part\n",
    "8. (optional) Try the best performing model with more iterations and aneal the model\n",
    "9. (optional) Run Tensorboard, what do you see iTry the best performing model with more iterations and aneal the model"
   ]
  }
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